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ecological-engine/cfg/operators.xml

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<operators name="core">
<!-- Weka operators -->
<factory class = "com.rapidminer.tools.WekaOperatorFactory"/>
<!-- Main Operators -->
<operator
name = "OperatorChain"
class = "com.rapidminer.operator.SimpleOperatorChain"
description = "A chain of operators that is subsequently applied."
icon = "chain"/>
<operator
name = "ModelApplier"
class = "com.rapidminer.operator.ModelApplier"
description = "Applies a model to an example set. This might be a prediction or another data transformation model."
icon = "model_applier"/>
<operator
name = "ModelUpdater"
class = "com.rapidminer.operator.ModelUpdater"
description = "Updates a model according to an example set. Please note that this operator can only be used for updatable models, otherwise an error will be shown."
icon = "model_applier"/>
<operator
name = "ModelGrouper"
class = "com.rapidminer.operator.ModelGrouper"
description = "Groups the input models into a single combined model which might be necessary for example for applying preprocessing models."
icon = "link_add"/>
<operator
name = "ModelUngrouper"
class = "com.rapidminer.operator.ModelUngrouper"
description = "Ungroups a previously grouped model into the single models which might then be handled on their own."
icon = "link_delete"/>
<!-- Core -->
<operator
name = "Process"
class = "com.rapidminer.operator.ProcessRootOperator"
description = "The root operator chain, which needs to be the outer most operator of any process."
icon = "chain"
group = "Core"/>
<operator
name = "Experiment"
class = "com.rapidminer.operator.ProcessRootOperator"
description = "The root operator chain, which needs to be the outer most operator of any experiment."
icon = "chain"
deprecation = "Please use the operator 'Process' instead."
group = "Core"/>
<operator
name = "MemoryCleanUp"
class = "com.rapidminer.operator.MemoryCleanUp"
description = "Frees unused memory. Might be useful after large preprocessing chains with a lot of (now) unused views or even data copies. Can be very useful after the data set was (again) materialized in memory."
icon = "clean_up"
group = "Core"/>
<operator
name = "MaterializeDataInMemory"
class = "com.rapidminer.operator.preprocessing.MaterializeDataInMemory"
description = "Creates a fresh and clean copy of the data. Might be useful after large preprocessing chains with a lot of views or even data copies, especially in combination with a memory clean up operation followed afterwards."
icon = "materialize"
group = "Core"/>
<operator
name = "IOConsumer"
class = "com.rapidminer.operator.IOConsumeOperator"
description = "This operators simply consumes some unused outputs."
icon = "io_consumer"
group = "Core.Objects"/>
<operator
name = "IOMultiplier"
class = "com.rapidminer.operator.IOMultiplyOperator"
description = "This operators simply multiplies selected input objects."
icon = "io_multiplier"
group = "Core.Objects"/>
<operator
name = "IOSelector"
class = "com.rapidminer.operator.IOSelectOperator"
description = "This operators simply selects one of the input objects of the specified type and brings it to the front so that following operators will work on this object."
icon = "io_selector"
group = "Core.Objects"/>
<operator
name = "IOStorer"
class = "com.rapidminer.operator.IOStorageOperator"
description = "This operators stores one of the input objects of the specified type into the process object storage."
icon = "io_storer"
group = "Core.Objects"/>
<operator
name = "IORetriever"
class = "com.rapidminer.operator.IORetrievalOperator"
description = "This operators retrieves an objects of the specified type which was previously stored into the process object storage."
icon = "io_retriever"
group = "Core.Objects"/>
<operator
name = "CommandLineOperator"
class = "com.rapidminer.operator.CommandLineOperator"
description = "This operator simply executes a command in a shell of the underlying operating system, basically any system command or external program."
icon = "command"
group = "Core"/>
<operator
name = "MacroDefinition"
class = "com.rapidminer.operator.MacroDefinitionOperator"
description = "This operator can be used to define arbitrary macros which can be used by %{my_macro} in parameter values."
icon = "macro"
group = "Core.Macros"/>
<operator
name = "SingleMacroDefinition"
class = "com.rapidminer.operator.SingleMacroDefinitionOperator"
description = "This operator can be used to define a single arbitrary macro which can be used by %{my_macro} in parameter values."
icon = "macro"
group = "Core.Macros"/>
<operator
name = "DataMacroDefinition"
class = "com.rapidminer.operator.DataMacroDefinitionOperator"
description = "This operator can be used to define a single macro which can be used by %{my_macro} in parameter values. The macro value will be derived from the input data."
icon = "macro"
group = "Core.Macros"/>
<operator
name = "MacroConstruction"
class = "com.rapidminer.operator.MacroConstructionOperator"
description = "This operator can be used to calculate new macros (from existing ones)."
icon = "macro"
group = "Core.Macros"/>
<operator
name = "FileEcho"
class = "com.rapidminer.operator.FileEchoOperator"
description = "This operator simply writes the given text into the specified file (can be useful in combination with a process branch)."
icon = "command"
group = "Core"/>
<operator
name = "SQLExecution"
class = "com.rapidminer.operator.SQLExecution"
description = "This operator simply performs an arbitrary SQL statement."
icon = "sql_execution"
group = "Core"/>
<operator
name = "Script"
class = "com.rapidminer.operator.ScriptingOperator"
description = "This operator executes arbitrary Groovy scripts."
icon = "sql_execution"
group = "Core"/>
<!-- Validation -->
<operator
name = "PerformanceEvaluator"
class = "com.rapidminer.operator.performance.PerformanceEvaluator"
description = "A performance evaluator delivers as output a list of performance values according to a list of performance criteria."
deprecation = "Please use the operators BasicPerformance, RegressionPerformance, ClassificationPerformance, or BinominalClassificationPerformance instead."
group = "Validation.Performance"/>
<operator
name = "CostEvaluator"
class = "com.rapidminer.operator.performance.cost.CostEvaluator"
description = "A cost evaluator delivers as output the costs for given classification results."
group = "Validation.Performance"/>
<operator
name = "Data2Performance"
class = "com.rapidminer.operator.performance.Data2Performance"
description = "This operator can be used to directly derive a performance measure from a specific data or statistics value."
group = "Validation.Performance"/>
<operator
name = "Performance"
class = "com.rapidminer.operator.performance.SimplePerformanceEvaluator"
description = "This operator delivers as output a list of performance values automatically determined in order to fit the learning task type."
group = "Validation"/>
<operator
name = "RegressionPerformance"
class = "com.rapidminer.operator.performance.RegressionPerformanceEvaluator"
description = "This operator delivers as output a list of performance values according to a list of selected performance criteria (for regression tasks)."
group = "Validation"/>
<operator
name = "ClassificationPerformance"
class = "com.rapidminer.operator.performance.PolynominalClassificationPerformanceEvaluator"
description = "This operator delivers as output a list of performance values according to a list of selected performance criteria (for all classification tasks)."
group = "Validation"/>
<operator
name = "BinominalClassificationPerformance"
class = "com.rapidminer.operator.performance.BinominalClassificationPerformanceEvaluator"
description = "This operator delivers as output a list of performance values according to a list of selected performance criteria (for binominal classification tasks)."
group = "Validation"/>
<operator
name = "ForecastingPerformance"
class = "com.rapidminer.operator.performance.ForecastingPerformanceEvaluator"
description = "This operator delivers as output a list of performance values according to a list of selected performance criteria (for forecasting regression tasks)."
group = "Validation"/>
<operator
name = "UserBasedPerformance"
class = "com.rapidminer.operator.performance.UserBasedPerformanceEvaluator"
description = "This operator delivers as output a list of performance values according to a list of user defined performance criteria."
group = "Validation.Performance"/>
<operator
name = "AttributeCounter"
class = "com.rapidminer.operator.performance.AttributeCounter"
description = "This operator created a performance vector containing the number of features of the input example set."
group = "Validation.Performance"/>
<operator
name = "SupportVectorCounter"
class = "com.rapidminer.operator.performance.SupportVectorCounter"
description = "This operator created a performance vector containing the number of support vectors of the input kernel model."
group = "Validation.Performance"/>
<operator
name = "WeightedPerformanceCreator"
class = "com.rapidminer.operator.performance.WeightedPerformanceCreator"
description = "Returns a performance vector containing the weighted fitness value of the input criteria."
group = "Validation.Performance"/>
<operator
name = "MinMaxWrapper"
class = "com.rapidminer.operator.performance.MinMaxWrapper"
description = "Puts all input criteria into a min-max criterion which delivers the minimum instead of the average or arbitrary weighted combinations."
group = "Validation.Performance"/>
<operator
name = "XValidation"
class = "com.rapidminer.operator.validation.XValidation"
description = "XValidation encapsulates a cross-validation in order to estimate the performance of a learning operator."
group = "Validation"/>
<operator
name = "BatchXValidation"
class = "com.rapidminer.operator.validation.BatchXValidation"
description = "A batched cross-validation in order to estimate the performance of a learning operator according to predefined example batches."
group = "Validation.Other"/>
<operator
name = "BootstrappingValidation"
class = "com.rapidminer.operator.validation.BootstrappingValidation"
description = "This operator encapsulates an iterated bootstrapping sampling with performance evaluation on the remaining examples."
group = "Validation.Other"/>
<operator
name = "WeightedBootstrappingValidation"
class = "com.rapidminer.operator.validation.WeightedBootstrappingValidation"
description = "This operator encapsulates an iterated weighted bootstrapping sampling with performance evaluation on the remaining examples."
group = "Validation.Other"/>
<operator
name = "SimpleValidation"
class = "com.rapidminer.operator.validation.RandomSplitValidationChain"
description = "A SimpleValidation randomly splits up the example set into a training and test set and evaluates the model."
group = "Validation"/>
<operator
name = "FixedSplitValidation"
class = "com.rapidminer.operator.validation.FixedSplitValidationChain"
description = "A FixedSplitValidation splits up the example set at a fixed point into a training and test set and evaluates the model."
group = "Validation.Other"/>
<operator
name = "SlidingWindowValidation"
class = "com.rapidminer.operator.validation.SlidingWindowValidation"
description = "SlidingWindoValidation encapsulates sliding windows of training and tests in order to estimate the performance of a prediction operator."
group = "Validation.Other"/>
<operator
name = "BatchSlidingWindowValidation"
class = "com.rapidminer.operator.validation.BatchSlidingWindowValidation"
description = "Performs a sliding window validation on predefined example batches."
group = "Validation.Other"/>
<operator
name = "WrapperXValidation"
class = "com.rapidminer.operator.validation.WrapperXValidation"
description = "Encapsulates a cross-validation to evaluate a feature weighting or selection method (wrapper)."
group = "Validation"/>
<operator
name = "SimpleWrapperValidation"
class = "com.rapidminer.operator.validation.RandomSplitWrapperValidationChain"
description = "A simple validation method to check the performance of a feature weighting or selection wrapper."
group = "Validation.Other"/>
<operator
name = "IteratingPerformanceAverage"
class = "com.rapidminer.operator.validation.IteratingPerformanceAverage"
description = "Iterates the inner operators and builds the average of the results."
group = "Validation.Other"/>
<operator
name = "CFSFeatureSetEvaluator"
class = "com.rapidminer.operator.validation.CFSFeatureSetEvaluator"
description = "Calculates a performance measure based on the Correlation (filter evaluation)."
group = "Validation.Performance"/>
<operator
name = "ConsistencyFeatureSetEvaluator"
class = "com.rapidminer.operator.validation.ConsistencyFeatureSetEvaluator"
description = "Calculates a performance measure based on the consistency (filter evaluation)."
group = "Validation.Performance"/>
<operator
name = "T-Test"
class = "com.rapidminer.operator.validation.significance.TTestSignificanceTestOperator"
description = "Performs a t-test to determine the probability for the null hypothesis 'the actual means are the same'."
group = "Validation.Significance"/>
<operator
name = "Anova"
class = "com.rapidminer.operator.validation.significance.AnovaSignificanceTestOperator"
description = "Performs ANalysis Of VAriances to determine the probability for the null hypothesis 'the actual means are the same'."
group = "Validation.Significance"/>
<operator
name = "ClusterDensityEvaluator"
class = "com.rapidminer.operator.validation.clustering.ClusterDensityEvaluator"
description = "Delivers a performance based on cluster densities."
group = "Validation.Performance.Clustering"/>
<operator
name = "ClusterCentroidEvaluator"
class = "com.rapidminer.operator.validation.clustering.CentroidBasedEvaluator"
description = "Delivers a performance based on cluster centroids."
group = "Validation.Performance.Clustering"/>
<operator
name = "ClusterNumberEvaluator"
class = "com.rapidminer.operator.validation.clustering.ClusterNumberEvaluator"
description = "Delivers a performance based on the number of clusters."
group = "Validation.Performance.Clustering"/>
<operator
name = "ItemDistributionEvaluator"
class = "com.rapidminer.operator.validation.clustering.exampledistribution.ExampleDistributionEvaluator"
description = "Delivers a performance of a cluster model based on the distribution of examples."
group = "Validation.Performance.Clustering"/>
<!-- IO -->
<operator
name = "AttributeConstructionsWriter"
class = "com.rapidminer.operator.io.AttributeConstructionsWriter"
description = "Writes all attributes of an example set to a file. Each line holds the construction description of one attribute."
group = "IO.Attributes"/>
<operator
name = "AttributeConstructionsLoader"
class = "com.rapidminer.operator.io.AttributeConstructionsLoader"
description = "Loads all attributes of an example set from a file. Each line holds the construction description of one attribute."
group = "IO.Attributes"/>
<operator
name = "AttributeWeightsWriter"
class = "com.rapidminer.operator.io.AttributeWeightsWriter"
description = "Writes the weights of all attributes of an example set to a file. Each line holds the name and the weight of one attribute."
group = "IO.Attributes"/>
<operator
name = "AttributeWeightsLoader"
class = "com.rapidminer.operator.io.AttributeWeightsLoader"
description = "Reads the weights of all attributes of an example set from a file. Each line must hold the name and the weight of one attribute."
group = "IO.Attributes"/>
<operator
name = "AccessExampleSource"
class = "com.rapidminer.operator.io.AccessExampleSource"
description = "This operator reads an example set from an Access database."
group = "IO.Examples"/>
<operator
name = "DatabaseExampleSource"
class = "com.rapidminer.operator.io.DatabaseExampleSource"
description = "This operator reads an example set from an SQL database."
group = "IO.Examples"/>
<operator
name = "CachedDatabaseExampleSource"
class = "com.rapidminer.operator.io.CachedDatabaseExampleSource"
description = "This operator reads an example set from an SQL database by incrementally caching it (recommended)."
group = "IO.Examples"/>
<operator
name = "SimpleExampleSource"
class = "com.rapidminer.operator.io.SimpleExampleSource"
description = "This operator reads an example set from file. It is a simpler version of the ExampleSource operator."
group = "IO.Examples"/>
<operator
name = "URLExampleSource"
class = "com.rapidminer.operator.io.URLExampleSource"
description = "This operator reads an example set from a URL. It allows only a fixed data format but on the other hand is able to read data from arbitrary sources."
group = "IO.Examples"/>
<operator
name = "ExampleSource"
class = "com.rapidminer.operator.io.ExampleSource"
description = "This operator reads an example set from file. The operator can be configured to read almost all file formats."
group = "IO.Examples"/>
<operator
name = "ExcelExampleSource"
class = "com.rapidminer.operator.io.ExcelExampleSource"
description = "This operator reads an example set from Excel spreadsheet files."
group = "IO.Examples"/>
<operator
name = "ExcelExampleSetWriter"
class = "com.rapidminer.operator.io.ExcelExampleSetWriter"
description = "This operator writes an example set to Excel spreadsheet files."
group = "IO.Examples"/>
<operator
name = "ExampleSetWriter"
class = "com.rapidminer.operator.io.ExampleSetWriter"
description = "Writes the values of all examples to a file."
group = "IO.Examples"/>
<operator
name = "DatabaseExampleSetWriter"
class = "com.rapidminer.operator.io.DatabaseExampleSetWriter"
description = "Writes the values of all examples to a single table in a database."
group = "IO.Examples"/>
<operator
name = "ArffExampleSetWriter"
class = "com.rapidminer.operator.io.ArffExampleSetWriter"
description = "Writes the values of all examples into an ARFF-file."
group = "IO.Examples"/>
<operator
name = "XrffExampleSetWriter"
class = "com.rapidminer.operator.io.XrffExampleSetWriter"
description = "Writes the values of all examples into an XRFF-file."
group = "IO.Examples"/>
<operator
name = "GnuplotWriter"
class = "com.rapidminer.operator.io.GNUPlotOperator"
description = "Creates gnuplot files from the data generated by a process log operator."
group = "IO.Other"/>
<operator
name = "ArffExampleSource"
class = "com.rapidminer.operator.io.ArffExampleSource"
description = "This operator can read arff files."
group = "IO.Examples"/>
<operator
name = "XrffExampleSource"
class = "com.rapidminer.operator.io.XrffExampleSource"
description = "This operator can read xrff files."
group = "IO.Examples"/>
<operator
name = "CSVExampleSource"
class = "com.rapidminer.operator.io.CSVExampleSource"
description = "This operator can read csv files."
group = "IO.Examples"/>
<operator
name = "CSVExampleSetWriter"
class = "com.rapidminer.operator.io.CSVExampleSetWriter"
description = "This operator can write csv files."
group = "IO.Examples"/>
<operator
name = "DBaseExampleSource"
class = "com.rapidminer.operator.io.DBaseExampleSource"
description = "This operator can read dBase files."
group = "IO.Examples"/>
<operator
name = "BibtexExampleSource"
class = "com.rapidminer.operator.io.BibtexExampleSource"
description = "This operator can read BibTeX files."
group = "IO.Examples"/>
<operator
name = "C45ExampleSource"
class = "com.rapidminer.operator.io.C45ExampleSource"
description = "This operator can read data and meta given in C4.5 format."
group = "IO.Examples"/>
<operator
name = "DasyLabExampleSource"
class = "com.rapidminer.operator.io.DasyLabDataReader"
description = "This operator can read DasyLab data files."
group = "IO.Examples"/>
<operator
name = "SPSSExampleSource"
class = "com.rapidminer.operator.io.SPSSExampleSource"
description = "This operator can read SPSS data files."
group = "IO.Examples"/>
<operator
name = "StataExampleSource"
class = "com.rapidminer.operator.io.StataExampleSource"
description = "This operator can read Stata data files."
group = "IO.Examples"/>
<operator
name = "SparseFormatExampleSource"
class = "com.rapidminer.operator.io.SparseFormatExampleSource"
description = "Reads an example file in sparse format."
group = "IO.Examples"/>
<operator
name = "ModelLoader"
class = "com.rapidminer.operator.io.ModelLoader"
description = "Reads a model from a given file."
group = "IO.Models"/>
<operator
name = "ModelWriter"
class = "com.rapidminer.operator.io.ModelWriter"
description = "Writes a model to a given file."
group = "IO.Models"/>
<operator
name = "WekaModelLoader"
class = "com.rapidminer.operator.io.WekaModelLoader"
description = "Reads a Weka model from a given file."
group = "IO.Models"/>
<operator
name = "ParameterSetLoader"
class = "com.rapidminer.operator.io.ParameterSetLoader"
description = "Reads a parameter set from a file."
group = "IO.Other"/>
<operator
name = "ParameterSetWriter"
class = "com.rapidminer.operator.io.ParameterSetWriter"
description = "Writes a parameter set into a file."
group = "IO.Other"/>
<operator
name = "ThresholdLoader"
class = "com.rapidminer.operator.io.ThresholdLoader"
description = "Loads a threshold from a file (used for transforming soft into crisp predictions)."
group = "IO.Other"/>
<operator
name = "ThresholdWriter"
class = "com.rapidminer.operator.io.ThresholdWriter"
description = "Writes a threshold to a file (used for transforming soft into crisp predictions)."
group = "IO.Other"/>
<operator
name = "IOContainerWriter"
class = "com.rapidminer.operator.io.IOContainerWriter"
description = "Writes all current IO objects to a file."
group = "IO.Other"/>
<operator
name = "IOContainerReader"
class = "com.rapidminer.operator.io.IOContainerReader"
description = "Reads an IOContainer from a file."
group = "IO.Other"/>
<operator
name = "IOObjectWriter"
class = "com.rapidminer.operator.io.IOObjectWriter"
description = "Generic writer for all types of IOObjects."
group = "IO.Other"/>
<operator
name = "IOObjectReader"
class = "com.rapidminer.operator.io.IOObjectReader"
description = "Generic reader for all types of IOObjects."
group = "IO.Other"/>
<operator
name = "ResultWriter"
class = "com.rapidminer.operator.io.ResultWriter"
description = "This operator can be used at each point in an operator chain and and writes current results to the console or to a file."
group = "IO.Results"/>
<operator
name = "PerformanceWriter"
class = "com.rapidminer.operator.io.PerformanceWriter"
description = "This operator can be used to write the input performance into a file."
group = "IO.Results"/>
<operator
name = "PerformanceLoader"
class = "com.rapidminer.operator.io.PerformanceLoader"
description = "This operator can be used to load a performance vector from a file."
group = "IO.Results"/>
<operator
name = "ClusterModelWriter"
class = "com.rapidminer.operator.io.ClusterModelWriter"
description = "Writes a cluster model to a file."
group = "IO.Clustering" />
<operator
name = "ClusterModelReader"
class = "com.rapidminer.operator.io.ClusterModelReader"
description = "Reads a single cluster model from a file."
group = "IO.Clustering" />
<!-- Supervised Learning -->
<operator
name = "Binary2MultiClassLearner"
class = "com.rapidminer.operator.learner.meta.Binary2MultiClassLearner"
description = "Builds a classification model for multiple classes based on a binary learner."
group = "Learner.Supervised.Meta"/>
<operator
name = "RelativeRegression"
class = "com.rapidminer.operator.learner.meta.RelativeRegression"
description = "Learns a regression model for predictions relative to another attribute value."
group = "Learner.Supervised.Meta"/>
<operator
name = "MetaCost"
class = "com.rapidminer.operator.learner.meta.MetaCost"
description = "Builds a classification model using cost values from a given matrix."
group = "Learner.Supervised.Meta"/>
<operator
name = "CostBasedThresholdLearner"
class = "com.rapidminer.operator.learner.meta.CostBasedThresholdLearner"
description = "Determines confidence thresholds based on misclassification costs, also possible to define costs for the option non-classified."
group = "Learner.Supervised.Meta"/>
<operator
name = "NaiveBayes"
class = "com.rapidminer.operator.learner.bayes.NaiveBayes"
description = "Returns classification model using estimated normal distributions."
group = "Learner.Supervised.Bayes"/>
<operator
name = "KernelNaiveBayes"
class = "com.rapidminer.operator.learner.bayes.KernelNaiveBayes"
description = "Returns classification model using estimated kernel densities."
group = "Learner.Supervised.Bayes"/>
<operator
name = "AttributeBasedVote"
class = "com.rapidminer.operator.learner.lazy.AttributeBasedVotingLearner"
description = "Actually no learning scheme since the prediction is the average of all attribute values."
group = "Learner.Supervised.Lazy"/>
<operator
name = "NearestNeighbors"
class = "com.rapidminer.operator.learner.lazy.KNNLearner"
description = "Classification with k-NN based on an explicit similarity measure."
group = "Learner.Supervised.Lazy"/>
<operator
name = "Perceptron"
class = "com.rapidminer.operator.learner.functions.Perceptron"
description = "Single Perceptron finding seperating hyperplane if one exists"
group = "Learner.Supervised.Functions"/>
<operator
name = "LinearRegression"
class = "com.rapidminer.operator.learner.functions.LinearRegression"
description = "Linear regression."
group = "Learner.Supervised.Functions"/>
<operator
name = "VectorLinearRegression"
class = "com.rapidminer.operator.learner.functions.VectorLinearRegression"
description = "Vector linear regression."
group = "Learner.Supervised.Functions"/>
<operator
name = "FastLargeMargin"
class = "com.rapidminer.operator.learner.functions.FastLargeMargin"
description = "A fast learning method for large margin optimizations."
group = "Learner.Supervised.Functions"/>
<operator
name = "PolynomialRegression"
class = "com.rapidminer.operator.learner.functions.PolynomialRegression"
description = "Polynomial regression."
group = "Learner.Supervised.Functions"/>
<operator
name = "AdditiveRegression"
class = "com.rapidminer.operator.learner.meta.AdditiveRegression"
description = "Additive regression operator allowing all learners (not restricted to Weka learners)."
group = "Learner.Supervised.Meta"/>
<operator
name = "Bagging"
class = "com.rapidminer.operator.learner.meta.Bagging"
description = "Bagging operator allowing all learners (not restricted to Weka learners)."
group = "Learner.Supervised.Meta"/>
<operator
name = "Stacking"
class = "com.rapidminer.operator.learner.meta.Stacking"
description = "Uses the first inner learner to build a stacked model on top of the predictions of the other inner learners."
group = "Learner.Supervised.Meta"/>
<operator
name = "Vote"
class = "com.rapidminer.operator.learner.meta.Vote"
description = "Uses a majority vote (for classification) or the average (for regression) on top of the predictions of the other inner learners."
group = "Learner.Supervised.Meta"/>
<operator
name = "BayesianBoosting"
class = "com.rapidminer.operator.learner.meta.BayesianBoosting"
description = "Boosting operator based on Bayes' theorem."
group = "Learner.Supervised.Meta"/>
<operator
name = "SubgroupDiscovery"
class = "com.rapidminer.operator.learner.meta.SDRulesetInduction"
description = "A Subgroup Discovery meta learning scheme"
group = "Learner.Supervised.Meta"/>
<operator
name = "ExhaustiveSubgroupDiscovery"
class = "com.rapidminer.operator.learner.subgroups.SubgroupDiscovery"
description = "Performs an exhaustive subgroup discovery."
group = "Learner.Supervised.Subgroups"/>
<operator
name = "IteratingGSS"
class = "com.rapidminer.operator.learner.igss.IteratingGSS"
description = "Combines Generic Sequential Sampling by Scheffer/Wrobel with Knowledge-Based Sampling by Scholz."
deprecation = "This operator will not be supported in future releases"
group = "Learner.Supervised.Rules"/>
<operator
name = "AdaBoost"
class = "com.rapidminer.operator.learner.meta.AdaBoost"
description = "Boosting operator allowing all learners (not restricted to Weka learners)."
group = "Learner.Supervised.Meta"/>
<operator
name = "BestRuleInduction"
class = "com.rapidminer.operator.learner.rules.BestRuleInduction"
description = "Returns a best conjunctive rule with respect to the WRAcc metric for boolean prediction problems and polynomial attributes."
group = "Learner.Supervised.Rules"/>
<operator
name = "MultiCriterionDecisionStump"
class = "com.rapidminer.operator.learner.tree.MultiCriterionDecisionStumps"
description = "A quick DecisionStump clone that allows to specify different utility functions."
group = "Learner.Supervised.Trees"/>
<operator
name = "NeuralNet"
class = "com.rapidminer.operator.learner.functions.neuralnet.NeuralNetLearner"
description = "Learns a neural net from the input data."
deprecation = "Please use the operator 'NeuralNetImproved' instead."
group = "Learner.Supervised.Functions"/>
<operator
name = "NeuralNetSimple"
class = "com.rapidminer.operator.learner.functions.neuralnet.SimpleNeuralNetLearner"
description = "Learns a neural net from the input data."
deprecation = "Please use the operator 'NeuralNetImproved' instead."
group = "Learner.Supervised.Functions"/>
<operator
name = "NeuralNetImproved"
class = "com.rapidminer.operator.learner.functions.neuralnet.ImprovedNeuralNetLearner"
description = "Learns a neural net from the input data."
group = "Learner.Supervised.Functions"/>
<operator
name = "DefaultLearner"
class = "com.rapidminer.operator.learner.lazy.DefaultLearner"
description = "Learns a default value."
group = "Learner.Supervised.Lazy"/>
<operator
name = "TransformedRegression"
class = "com.rapidminer.operator.learner.meta.TransformedRegression"
description = "This learner performs regression by transforming the labels and calling an inner regression learner."
group = "Learner.Supervised.Meta"/>
<operator
name = "ClassificationByRegression"
class = "com.rapidminer.operator.learner.meta.ClassificationByRegression"
description = "This operator chain must contain a regression learner and allows to learn classifications tasks with more than two classes."
group = "Learner.Supervised.Meta"/>
<operator
name = "JMySVMLearner"
class = "com.rapidminer.operator.learner.functions.kernel.JMySVMLearner"
description = "JMySVMLearner provides an internal Java implementation of the mySVM by Stefan Rueping."
group = "Learner.Supervised.Functions"/>
<operator
name = "MyKLRLearner"
class = "com.rapidminer.operator.learner.functions.kernel.MyKLRLearner"
description = "MyKLRLearner provides an internal Java implementation of the myKLR by Stefan Rueping."
group = "Learner.Supervised.Functions"/>
<operator
name = "LibSVMLearner"
class = "com.rapidminer.operator.learner.functions.kernel.LibSVMLearner"
description = "LibSVMLearner encapsulates the Java libsvm, an SVM learner."
group = "Learner.Supervised.Functions"/>
<operator
name = "EvoSVM"
class = "com.rapidminer.operator.learner.functions.kernel.evosvm.EvoSVM"
description = "EvoSVM uses an Evolutionary Strategy for optimization."
group = "Learner.Supervised.Functions"/>
<operator
name = "HyperHyper"
class = "com.rapidminer.operator.learner.functions.kernel.hyperhyper.HyperHyper"
description = "This is a minimal SVM implementation. The model is built with only one positive and one negative example. Typically this operater is used in combination with a boosting method."
group = "Learner.Supervised.Functions"/>
<operator
name = "LogisticRegression"
class = "com.rapidminer.operator.learner.functions.LogisticRegression"
description = "A logistic regression learner for binary classification tasks."
group = "Learner.Supervised.Functions"/>
<operator
name = "LinearDiscriminantAnalysis"
class = "com.rapidminer.operator.learner.bayes.LinearDiscriminantAnalysis"
description = "A linear discriminant function for binominal labels and numerical attributes."
group = "Learner.Supervised.Functions"/>
<operator
name = "QuadraticDiscriminantAnalysis"
class = "com.rapidminer.operator.learner.bayes.QuadraticDiscriminantAnalysis"
description = "A quadratic discriminant function for binominal labels and numerical attributes."
group = "Learner.Supervised.Functions"/>
<operator
name = "RegularizedDiscriminantAnalysis"
class = "com.rapidminer.operator.learner.bayes.RegularizedDiscriminantAnalysis"
description = "A regularized generale discriminant function for binominal labels and numerical attributes."
group = "Learner.Supervised.Functions"/>
<operator
name = "KernelLogisticRegression"
class = "com.rapidminer.operator.learner.functions.kernel.KernelLogisticRegression"
description = "A kernel logistic regression learner for binary classification tasks."
group = "Learner.Supervised.Functions"/>
<operator
name = "PsoSVM"
class = "com.rapidminer.operator.learner.functions.kernel.evosvm.PSOSVM"
description = "PsoSVM uses a Particle Swarm Optimization for optimization."
group = "Learner.Supervised.Functions"/>
<operator
name = "RVMLearner"
class = "com.rapidminer.operator.learner.functions.kernel.RVMLearner"
description = "An implementation of a relevance vector machine."
group = "Learner.Supervised.Functions"/>
<operator
name = "GPLearner"
class = "com.rapidminer.operator.learner.functions.kernel.GPLearner"
description = "An implementation of Gaussian Processes."
group = "Learner.Supervised.Functions"/>
<operator
name = "DecisionStump"
class = "com.rapidminer.operator.learner.tree.DecisionStumpLearner"
description = "Learns only a root node of a decision tree. Can be very efficient when boosted."
group = "Learner.Supervised.Trees"/>
<operator
name = "ID3"
class = "com.rapidminer.operator.learner.tree.ID3Learner"
description = "Learns an unpruned decision tree from nominal attributes only."
group = "Learner.Supervised.Trees"/>
<operator
name = "ID3Numerical"
class = "com.rapidminer.operator.learner.tree.ID3NumericalLearner"
description = "Learns an unpruned decision tree from nominal and numerical data."
group = "Learner.Supervised.Trees"/>
<operator
name = "DecisionTree"
class = "com.rapidminer.operator.learner.tree.DecisionTreeLearner"
description = "Learns a pruned decision tree which can handle both numerical and nominal attributes."
group = "Learner.Supervised.Trees"/>
<operator
name = "CHAID"
class = "com.rapidminer.operator.learner.tree.CHAIDLearner"
description = "Learns a pruned decision tree based on a chi squared attribute relevance test."
group = "Learner.Supervised.Trees"/>
<operator
name = "RelevanceTree"
class = "com.rapidminer.operator.learner.tree.RelevanceTreeLearner"
description = "Learns a pruned decision tree based on an arbitrary feature relevance test (attribute weighting scheme as inner operator)."
group = "Learner.Supervised.Trees"/>
<operator
name = "RandomTree"
class = "com.rapidminer.operator.learner.tree.RandomTreeLearner"
description = "Learns a single decision tree. For each split only a random subset of attributes is available."
group = "Learner.Supervised.Trees"/>
<operator
name = "RandomForest"
class = "com.rapidminer.operator.learner.tree.RandomForestLearner"
description = "Learns a set of random trees, i.e. for each split only a random subset of attributes is available. The resulting model is a voting model of all trees."
group = "Learner.Supervised.Trees"/>
<operator
name = "RuleLearner"
class = "com.rapidminer.operator.learner.rules.RuleLearner"
description = "Learns a pruned set of rules with respect to the information gain."
group = "Learner.Supervised.Rules"/>
<operator
name = "OneR"
class = "com.rapidminer.operator.learner.rules.SingleRuleLearner"
description = "Learns a single rule using only one attribute."
group = "Learner.Supervised.Rules"/>
<operator
name = "BasicRuleLearner"
class = "com.rapidminer.operator.learner.rules.SimpleRuleLearner"
description = "Learns a set of rules minimizing the training error without pruning."
group = "Learner.Supervised.Rules"/>
<operator
name = "Tree2RuleConverter"
class = "com.rapidminer.operator.learner.meta.Tree2RuleConverter"
description = "Determines a set of rules from a given decision tree model."
group = "Learner.Supervised.Meta"/>
<operator
name = "MultiwayDecisionTree"
class = "com.rapidminer.operator.learner.tree.MultiwayDecisionTree"
description = "Uses the split points of numerical attributes to first discretize attributes and then perform the tree building again."
group = "Learner.Supervised.Trees" />
<!-- Unsupervised Learning -->
<operator
name = "FPGrowth"
class = "com.rapidminer.operator.learner.associations.fpgrowth.FPGrowth"
description = "This learner efficiently calculates all frequent item sets from the given data."
group = "Learner.Unsupervised.Itemsets"/>
<operator
name = "AssociationRuleGenerator"
class = "com.rapidminer.operator.learner.associations.AssociationRuleGenerator"
description = "This operator generated a set of association rules for a given set of frequent item sets."
group = "Learner.Unsupervised.Itemsets"/>
<operator
name = "KMedoids"
class = "com.rapidminer.operator.clustering.clusterer.KMedoids"
description = "Clustering with k-medoids"
group = "Learner.Unsupervised.Clustering"/>
<operator
name = "KMeans"
class = "com.rapidminer.operator.clustering.clusterer.KMeans"
description = "Clustering with k-means"
group = "Learner.Unsupervised.Clustering"/>
<operator
name = "EMClustering"
class = "com.rapidminer.operator.clustering.clusterer.soft.EMClusterer"
description = "Clustering with EM"
group = "Learner.Unsupervised.Clustering"/>
<operator
name = "KernelKMeans"
class = "com.rapidminer.operator.clustering.clusterer.KernelKMeans"
description = "Clustering with kernel k-means"
group = "Learner.Unsupervised.Clustering"/>
<operator
name = "SupportVectorClustering"
class = "com.rapidminer.operator.clustering.clusterer.SVClustering"
description = "Clustering with support vectors"
group = "Learner.Unsupervised.Clustering"/>
<operator
name = "AgglomerativeClustering"
class = "com.rapidminer.operator.clustering.clusterer.AgglomerativeClustering"
description = "Agglomerative buttom-up clustering"
group = "Learner.Unsupervised.Clustering"/>
<operator
name = "TopDownClustering"
class = "com.rapidminer.operator.clustering.clusterer.TopDownClustering"
description = "Hierarchical clustering by applying an inner flat clusterer scheme recursively"
group = "Learner.Unsupervised.Clustering"/>
<operator
name = "DBScanClustering"
class = "com.rapidminer.operator.clustering.clusterer.DBScan"
description = "Clustering with DBSCAN"
group = "Learner.Unsupervised.Clustering"/>
<operator
name = "RandomFlatClustering"
class = "com.rapidminer.operator.clustering.clusterer.RandomClustering"
description = "Flat random clustering"
group = "Learner.Unsupervised.Clustering"/>
<operator
name = "ExampleSet2ClusterModel"
class = "com.rapidminer.operator.clustering.clusterer.ExampleSet2ClusterModel"
description = "Clustering based on one nominal attribute."
group = "Learner.Unsupervised.Clustering"/>
<operator
name = "FlattenClusterModel"
class = "com.rapidminer.operator.clustering.FlattenClusterModel"
description = "Creates a flat cluster model from a hierarchical one."
group = "Learner.Unsupervised.Clustering"/>
<operator
name = "ClusterModel2ExampleSet"
class = "com.rapidminer.operator.clustering.ClusterModel2ExampleSet"
description = "Labels an example set with the cluster ids from a given non hierarchical cluster model."
group = "Learner.Unsupervised.Clustering"/>
<operator
name = "Cluster2Prediction"
class = "com.rapidminer.operator.clustering.ClusterToPrediction"
description = "This operator converts the cluster attribute into an prediction attribut, choosing the best fitting pairs between cluster and label."
group = "Learner.Unsupervised.Clustering"/>
<operator
name = "ExampleSet2Similarity"
class = "com.rapidminer.operator.similarity.ExampleSet2Similarity"
description = "Calculates a similarity measure from the given data (attribute based)."
group = "Learner.Unsupervised.Clustering.Similarity"/>
<operator
name = "ExampleSet2SimilarityExampleSet"
class = "com.rapidminer.operator.similarity.ExampleSet2SimilarityExampleSet"
description = "Calculates between all given examples."
group = "Learner.Unsupervised.Clustering.Similarity"/>
<operator
name = "Similarity2ExampleSet"
class = "com.rapidminer.operator.similarity.Similarity2ExampleSet"
description = "Calculates a an example set from a similarity measure."
group = "Learner.Unsupervised.Clustering.Similarity"/>
<!-- Data preprocessing -->
<operator
name = "IdTagging"
class = "com.rapidminer.operator.preprocessing.IdTagging"
description = "Adds a new id attribute to the example set, each example is tagged with an incremented number."
group = "Preprocessing"/>
<operator
name = "AttributeSubsetPreprocessing"
class = "com.rapidminer.operator.preprocessing.AttributeSubsetPreprocessing"
description = "Selects one attribute (or a subset) via a regular expression and applies its inner operators to the resulting subset."
group = "Preprocessing.Attributes"/>
<operator
name = "AbsoluteValues"
class = "com.rapidminer.operator.preprocessing.filter.AbsoluteValueFilter"
description = "Replaces all numerical values by their absolute values."
group = "Preprocessing.Attributes.Generation"/>
<operator
name = "AttributeWeightsApplier"
class = "com.rapidminer.operator.features.AttributeWeightsApplier"
description = "Deselects attributes with weight 0 and calculates new values for numeric attributes."
group = "Preprocessing.Attributes"/>
<operator
name = "PrincipalComponentsGenerator"
class = "com.rapidminer.operator.features.transformation.PrincipalComponentsTransformation"
description = "Build the principal components of the given data."
group = "Preprocessing.Attributes.Transformation"/>
<operator
name = "FrequentItemSetAttributeCreator"
class = "com.rapidminer.operator.learner.associations.FrequentItemSetAttributeCreator"
description = "Creates attributes from frequent item sets."
group = "Preprocessing.Attributes.Generation"/>
<operator
name = "AttributeMerge"
class = "com.rapidminer.operator.preprocessing.filter.AttributeMerge"
description = "Merges two attributes into a single new attribute by concatenating the values."
group = "Preprocessing.Attributes.Filter"/>
<operator
name = "ChangeAttributeName"
class = "com.rapidminer.operator.preprocessing.filter.ChangeAttributeName"
description = "This operator can be used to rename an attribute."
group = "Preprocessing.Attributes.Filter"/>
<operator
name = "ChangeAttributeNamesReplace"
class = "com.rapidminer.operator.preprocessing.filter.ChangeAttributeNamesReplace"
description = "This operator can be used to rename a set of attributes by replacing parts of the attribute names by a specified replacement."
group = "Preprocessing.Attributes.Filter"/>
<operator
name = "ChangeAttributeNames2Generic"
class = "com.rapidminer.operator.preprocessing.filter.ChangeAttributeNames2Generic"
description = "This operator can be used to rename all attributes of the input example set to a set of generic names like att1, att2, att3 etc."
group = "Preprocessing.Attributes.Filter"/>
<operator
name = "Construction2Names"
class = "com.rapidminer.operator.preprocessing.filter.Construction2Names"
description = "This operator renames all regular attributes by their construction descriptions if available."
group = "Preprocessing.Attributes.Filter"/>
<operator
name = "ChangeAttributeType"
class = "com.rapidminer.operator.preprocessing.filter.ChangeAttributeType"
description = "This operator can be used to change the attribute type (regular, special, label, id...)."
deprecation = "Please use the operator ChangeAttributeRole instead"
group = "Preprocessing.Attributes.Filter"/>
<operator
name = "ChangeAttributeRole"
class = "com.rapidminer.operator.preprocessing.filter.ChangeAttributeRole"
description = "This operator can be used to change the attribute type (regular, special, label, id...)."
group = "Preprocessing.Attributes.Filter"/>
<operator
name = "ExchangeAttributeRoles"
class = "com.rapidminer.operator.preprocessing.filter.ExchangeAttributeRoles"
description = "This operator can be used to exchange the attribute roles of two attributes (e.g. a label with a regular attribute)."
group = "Preprocessing.Attributes.Filter"/>
<operator
name = "AttributeWeightSelection"
class = "com.rapidminer.operator.features.selection.AttributeWeightSelection"
description = "Selects only attributes which weights fulfill a given relation with respect to the input attribute weights."
group = "Preprocessing.Attributes.Selection"/>
<operator
name = "BruteForce"
class = "com.rapidminer.operator.features.selection.BruteForceSelection"
description = "Selects the best features for an example set by trying all possible combinations of attribute selections."
group = "Preprocessing.Attributes.Selection"/>
<operator
name = "FeatureSelection"
class = "com.rapidminer.operator.features.selection.FeatureSelectionOperator"
description = "This operator realizes feature selection by forward selection and backward elimination, respectively."
group = "Preprocessing.Attributes.Selection"/>
<operator
name = "RandomSelection"
class = "com.rapidminer.operator.features.selection.RandomSelection"
description = "This operator simply selects a random or a predefined number of random features."
group = "Preprocessing.Attributes.Selection"/>
<operator
name = "GeneticAlgorithm"
class = "com.rapidminer.operator.features.selection.GeneticAlgorithm"
description = "A genetic algorithm for feature selection."
group = "Preprocessing.Attributes.Selection"/>
<operator
name = "WeightGuidedFeatureSelection"
class = "com.rapidminer.operator.features.selection.WeightGuidedSelectionOperator"
description = "Adds iteratively features according to input attribute weights"
group = "Preprocessing.Attributes.Selection"/>
<operator
name = "WeightOptimization"
class = "com.rapidminer.operator.meta.WeightOptimization"
description = "Feature selection (forward, backward) guided by weights."
group = "Preprocessing.Attributes.Selection"/>
<operator
name = "IterativeWeightOptimization"
class = "com.rapidminer.operator.meta.IterativeWeightOptimization"
description = "Feature selection (forward, backward) guided by weights. Weights have to be updated after each iteration"
group = "Preprocessing.Attributes.Selection"/>
<operator
name = "GeneratingGeneticAlgorithm"
class = "com.rapidminer.operator.features.construction.GeneratingGeneticAlgorithm"
description = "A genetic algorithm for feature selection and feature generation (GGA)."
group = "Preprocessing.Attributes.Generation"/>
<operator
name = "AGA"
class = "com.rapidminer.operator.features.construction.AGA"
description = "Another (improved) genetic algorithm for feature selection and feature generation (AGA)."
group = "Preprocessing.Attributes.Generation"/>
<operator
name = "YAGGA"
class = "com.rapidminer.operator.features.construction.YAGGA"
description = "Yet Another GGA (Generating Geneting Algorithm). On average individuals (= selected attribute sets) will keep their original length, unless longer or shorther ones prove to have a better fitness."
group = "Preprocessing.Attributes.Generation"/>
<operator
name = "YAGGA2"
class = "com.rapidminer.operator.features.construction.YAGGA2"
description = "Improved version of Yet Another GGA (Generating Geneting Algorithm)."
group = "Preprocessing.Attributes.Generation"/>
<operator
name = "EvolutionaryFeatureAggregation"
class = "com.rapidminer.operator.features.aggregation.EvolutionaryFeatureAggregation"
description = "A generating genetic algorithm for unsupervised learning (experimental)."
group = "Preprocessing.Attributes.Aggregation"/>
<operator
name = "NameBasedWeighting"
class = "com.rapidminer.operator.features.weighting.NameBasedWeighting"
description = "This operator defines the weights for all features based on a list of regular expressions for the feature names which can be used to set a specified weight to features with a name fulfilling these expressions."
group = "Preprocessing.Attributes.Weighting"/>
<operator
name = "SingleRuleWeighting"
class = "com.rapidminer.operator.features.weighting.OneRErrorWeighting"
description = "This operator measures the relevance of features by constructing a single rule for each attribute and calculating the errors."
group = "Preprocessing.Attributes.Weighting"/>
<operator
name = "Relief"
class = "com.rapidminer.operator.features.weighting.ReliefWeighting"
description = "Relief measures the relevance of features by sampling examples and comparing the value of the current feature for the nearest example of the same and of a different class."
group = "Preprocessing.Attributes.Weighting"/>
<operator
name = "ChiSquaredWeighting"
class = "com.rapidminer.operator.features.weighting.ChiSquaredWeighting"
description = "This operator calculates the relevance of a feature by computing for each attribute of the input example set the value of the chi-squared statistic with respect to the class attribute."
group = "Preprocessing.Attributes.Weighting"/>
<operator
name = "SymmetricalUncertaintyWeighting"
class = "com.rapidminer.operator.features.weighting.SymmetricalUncertaintyOperator"
description = "This operator calculates the relevance of an attribute by measuring the symmetrical uncertainty with respect to the class."
group = "Preprocessing.Attributes.Weighting"/>
<operator
name = "ExampleSet2AttributeWeights"
class = "com.rapidminer.operator.features.weighting.ExampleSet2AttributeWeights"
description = "This operator simply creates new attribute weights of 1 for each input attribute."
group = "Preprocessing.Attributes.Weighting"/>
<operator
name = "AttributeWeights2ExampleSet"
class = "com.rapidminer.operator.features.weighting.AttributeWeights2ExampleSet"
description = "This operator simply creates a new data set where for each attribute the corresponding weight is stored."
group = "Preprocessing.Attributes.Weighting"/>
<operator
name = "ProcessLog2AttributeWeights"
class = "com.rapidminer.operator.features.weighting.ProcessLog2AttributeWeights"
description = "This operator creates new attribute weights from a attribute names column logged with the ProcessLog operator."
group = "Preprocessing.Attributes.Weighting"/>
<operator
name = "InfoGainWeighting"
class = "com.rapidminer.operator.features.weighting.InfoGainWeighting"
description = "This operator calculates the relevance of the attributes based on the information gain."
group = "Preprocessing.Attributes.Weighting"/>
<operator
name = "InfoGainRatioWeighting"
class = "com.rapidminer.operator.features.weighting.InfoGainRatioWeighting"
description = "This operator calculates the relevance of the attributes based on the information gain ratio."
group = "Preprocessing.Attributes.Weighting"/>
<operator
name = "CorrelationBasedWeighting"
class = "com.rapidminer.operator.features.weighting.CorrelationWeighting"
description = "Performs an example weighting based upon the residuals of an unweighted local polynomial regression."
group = "Preprocessing.Data.Weighting" />
<operator
name = "GiniIndexWeighting"
class = "com.rapidminer.operator.features.weighting.GiniWeighting"
description = "This operator calculates the relevance of the attributes based on the Gini impurity index."
group = "Preprocessing.Attributes.Weighting"/>
<operator
name = "SVMWeighting"
class = "com.rapidminer.operator.features.weighting.SVMWeighting"
description = "This operator uses the coefficients of a hyperplance calculated by an SVM as feature weights."
group = "Preprocessing.Attributes.Weighting"/>
<operator
name = "PCAWeighting"
class = "com.rapidminer.operator.features.weighting.PCAWeighting"
description = "This operator uses the factors of a PCA component (usually the first) as feature weights."
group = "Preprocessing.Attributes.Weighting"/>
<operator
name = "CorpusBasedWeighting"
class = "com.rapidminer.operator.features.weighting.CorpusBasedFeatureWeighting"
description = "This operator uses a corpus of examples to characterize a single class by setting feature weights."
group = "Preprocessing.Attributes.Weighting"/>
<operator
name = "ForwardWeighting"
class = "com.rapidminer.operator.features.weighting.ForwardWeighting"
description = "Assumes that features are independent and optimizes the weights of the attributes with a linear search."
group = "Preprocessing.Attributes.Weighting"/>
<operator
name = "BackwardWeighting"
class = "com.rapidminer.operator.features.weighting.BackwardWeighting"
description = "Assumes that features are independent and optimizes the weights of the attributes with a linear search."
group = "Preprocessing.Attributes.Weighting"/>
<operator
name = "EvolutionaryWeighting"
class = "com.rapidminer.operator.features.weighting.EvolutionaryWeighting"
description = "Weight the features with an evolutionary approach."
group = "Preprocessing.Attributes.Weighting"/>
<operator
name = "PSOWeighting"
class = "com.rapidminer.operator.features.weighting.PSOWeighting"
description = "Weight the features with a particle swarm optimization approach."
group = "Preprocessing.Attributes.Weighting"/>
<operator
name = "InteractiveAttributeWeighting"
class = "com.rapidminer.operator.features.weighting.InteractiveAttributeWeighting"
description = "Shows a window with feature weights and allows users to change them."
group = "Preprocessing.Attributes.Weighting"/>
<operator
name = "StandardDeviationWeighting"
class = "com.rapidminer.operator.features.weighting.StandardDeviationWeighting"
description = "Computes weights based on the (normalized) standard deviation of the attributes."
group = "Preprocessing.Attributes.Weighting"/>
<operator
name = "Attributes2RealValues"
class = "com.rapidminer.operator.preprocessing.filter.NominalToNumeric"
description = "Maps all values to real values."
deprecation = "Please use the operator 'Nominal2Numerical' or 'Numerical2Real' instead"
group = "Preprocessing.Attributes.Filter.Converter"/>
<operator
name = "NominalNumbers2Numerical"
class = "com.rapidminer.operator.preprocessing.filter.NominalNumbers2Numerical"
description = "Maps all nominal values to numerical values by parsing the numbers if possible."
group = "Preprocessing.Attributes.Filter.Converter"/>
<operator
name = "GuessValueTypes"
class = "com.rapidminer.operator.preprocessing.GuessValueTypes"
description = "(Re-)guesses all value types and changes them accordingly."
group = "Preprocessing.Attributes.Filter.Converter"/>
<operator
name = "Nominal2String"
class = "com.rapidminer.operator.preprocessing.filter.Nominal2String"
description = "Replaces all nominal attributes by corresponding string attributes."
group = "Preprocessing.Attributes.Filter.Converter"/>
<operator
name = "String2Nominal"
class = "com.rapidminer.operator.preprocessing.filter.String2Nominal"
description = "Replaces all string attributes by corresponding nominal attributes."
group = "Preprocessing.Attributes.Filter.Converter"/>
<operator
name = "Numerical2Real"
class = "com.rapidminer.operator.preprocessing.filter.Numerical2Real"
description = "Replaces all numerical attributes (especially integer attributes) by corresponding real valued attributes."
group = "Preprocessing.Attributes.Filter.Converter"/>
<operator
name = "Real2Integer"
class = "com.rapidminer.operator.preprocessing.filter.Real2Integer"
description = "Replaces all real-valued attributes by corresponding integer attributes (rounded or cutted)."
group = "Preprocessing.Attributes.Filter.Converter"/>
<operator
name = "Nominal2Date"
class = "com.rapidminer.operator.preprocessing.filter.Nominal2Date"
description = "Parses the nominal values for the specified attribute with respect to the given date format string and transforms the values into date values."
group = "Preprocessing.Attributes.Filter.Converter"/>
<operator
name = "Date2Nominal"
class = "com.rapidminer.operator.preprocessing.filter.Date2Nominal"
description = "Parses the date values for the specified date attribute with respect to the given date format string and transforms the values into nominal values."
group = "Preprocessing.Attributes.Filter.Converter"/>
<operator
name = "Date2Numerical"
class = "com.rapidminer.operator.preprocessing.filter.Date2Numerical"
description = "Transforms date values into numerical ones capturing the milliseconds since 01/01/1970 00:00:00 GMT."
group = "Preprocessing.Attributes.Filter.Converter"/>
<operator
name = "Nominal2Numeric"
class = "com.rapidminer.operator.preprocessing.filter.NominalToNumeric"
description = "Maps all values to real values (usually simply using the internal indices)."
deprecation = "Please use the operator 'Nominal2Numerical' instead"
group = "Preprocessing.Attributes.Filter.Converter"/>
<operator
name = "Nominal2Numerical"
class = "com.rapidminer.operator.preprocessing.filter.NominalToNumeric"
description = "Maps all values to real values (usually simply using the internal indices)."
group = "Preprocessing.Attributes.Filter.Converter"/>
<operator
name = "Numeric2Binary"
class = "com.rapidminer.operator.preprocessing.filter.NumericToBinominal"
description = "Maps all numeric values to 'false' if they are in the specified range (typical: equal 0.0) and to 'true' otherwise."
deprecation = "Please use the operator 'Numerical2Binominal' instead"
group = "Preprocessing.Attributes.Filter.Converter"/>
<operator
name = "Numeric2Binominal"
class = "com.rapidminer.operator.preprocessing.filter.NumericToBinominal"
description = "Maps all numeric values to 'false' if they are in the specified range (typical: equal 0.0) and to 'true' otherwise."
deprecation = "Please use the operator 'Numerical2Binominal' instead"
group = "Preprocessing.Attributes.Filter.Converter"/>
<operator
name = "Numerical2Binominal"
class = "com.rapidminer.operator.preprocessing.filter.NumericToBinominal"
description = "Maps all numeric values to 'false' if they are in the specified range (typical: equal 0.0) and to 'true' otherwise."
group = "Preprocessing.Attributes.Filter.Converter"/>
<operator
name = "Numeric2Polynominal"
class = "com.rapidminer.operator.preprocessing.filter.NumericToPolynominal"
description = "Maps all numeric values simply to the corresponding nominal values. Please use one of the discretization operators if you need more sophisticated nominalization methods."
deprecation = "Please use the operator 'Numerical2Polynominal' instead"
group = "Preprocessing.Attributes.Filter.Converter"/>
<operator
name = "Numerical2Polynominal"
class = "com.rapidminer.operator.preprocessing.filter.NumericToPolynominal"
description = "Maps all numeric values simply to the corresponding nominal values. Please use one of the discretization operators if you need more sophisticated nominalization methods."
group = "Preprocessing.Attributes.Filter.Converter"/>
<operator
name = "Numerical2FormattedNominal"
class = "com.rapidminer.operator.preprocessing.filter.NumericToFormattedNominal"
description = "Reformats all numerical attributes according to the specified settings and change the attributes to nominal."
group = "Preprocessing.Attributes.Filter.Converter"/>
<operator
name = "AttributeValueMapper"
class = "com.rapidminer.operator.preprocessing.filter.AttributeValueMapper"
description = "Maps certain values of an attribute to other values."
deprecation = "Please use the operator 'Mapping' instead."
group = "Preprocessing.Attributes.Filter.Values"/>
<operator
name = "Mapping"
class = "com.rapidminer.operator.preprocessing.filter.AttributeValueMapper"
description = "Maps certain values of an attribute to other values."
group = "Preprocessing.Attributes.Filter.Values"/>
<operator
name = "InternalBinominalRemapping"
class = "com.rapidminer.operator.preprocessing.filter.InternalBinominalRemapping"
description = "Corrects the internal value mapping of binominal attributes according to the specified negative and positive values."
group = "Preprocessing.Attributes.Filter.Values"/>
<operator
name = "AttributeValueSubstring"
class = "com.rapidminer.operator.preprocessing.filter.AttributeValueSubstring"
description = "Creates new attributes from nominal attributes which only contain substrings of the original attributes."
deprecation = "Please use the operator 'Substring' instead."
group = "Preprocessing.Attributes.Filter.Values"/>
<operator
name = "Substring"
class = "com.rapidminer.operator.preprocessing.filter.AttributeValueSubstring"
description = "Creates new attributes from nominal attributes which only contain substrings of the original attributes."
group = "Preprocessing.Attributes.Filter.Values"/>
<operator
name = "Trim"
class = "com.rapidminer.operator.preprocessing.filter.AttributeValueTrim"
description = "Creates new attributes from nominal attributes which contain the trimmed original values."
group = "Preprocessing.Attributes.Filter.Values"/>
<operator
name = "Replace"
class = "com.rapidminer.operator.preprocessing.filter.AttributeValueReplace"
description = "Creates new attributes from nominal attributes with replaced substrings."
group = "Preprocessing.Attributes.Filter.Values"/>
<operator
name = "Dictionary"
class = "com.rapidminer.operator.preprocessing.filter.ExampleSetToDictionary"
description = "Replaces occurrances in nominal attributes specified by a second input example set."
group = "Preprocessing.Attributes.Filter.Values"/>
<operator
name = "Split"
class = "com.rapidminer.operator.preprocessing.filter.AttributeValueSplit"
description = "Creates new attributes from a nominal attribute by dividing the nominal values into parts according to a split criterion."
group = "Preprocessing.Attributes.Filter.Values"/>
<operator
name = "AddNominalValue"
class = "com.rapidminer.operator.preprocessing.filter.AddNominalValue"
description = "This operator adds an additional value to a specified nominal attribute which is then mapped to a specific index."
deprecation = "Please use the operator 'AddValue' instead."
group = "Preprocessing.Attributes.Filter.Values"/>
<operator
name = "AddValue"
class = "com.rapidminer.operator.preprocessing.filter.AddNominalValue"
description = "This operator adds an additional value to a specified nominal attribute which is then mapped to a specific index."
group = "Preprocessing.Attributes.Filter.Values"/>
<operator
name = "MergeNominalValues"
class = "com.rapidminer.operator.preprocessing.filter.MergeNominalValues"
description = "Merges two nominal values of a specified attribute."
deprecation = "Please use the operator 'MergeValues' instead."
group = "Preprocessing.Attributes.Filter.Values"/>
<operator
name = "MergeValues"
class = "com.rapidminer.operator.preprocessing.filter.MergeNominalValues"
description = "Merges two nominal values of a specified attribute."
group = "Preprocessing.Attributes.Filter.Values"/>
<operator
name = "AttributeAdd"
class = "com.rapidminer.operator.preprocessing.filter.AttributeAdd"
description = "Adds a new attribute to the data set with the given name and type."
group = "Preprocessing.Attributes.Filter"/>
<operator
name = "AttributeCopy"
class = "com.rapidminer.operator.preprocessing.filter.AttributeCopy"
description = "Copies a single attribute (only the view on the data column, not the data itself)."
group = "Preprocessing.Attributes.Filter"/>
<operator
name = "BinDiscretization"
class = "com.rapidminer.operator.preprocessing.discretization.BinDiscretization"
description = "Discretize numerical attributes into a user defined number of bins."
group = "Preprocessing.Data.Discretization"/>
<operator
name = "MinMaxBinDiscretization"
class = "com.rapidminer.operator.preprocessing.discretization.MinMaxBinDiscretization"
description = "Discretize numerical attributes into a user defined number of bins lying in a user defined value range."
group = "Preprocessing.Data.Discretization"/>
<operator
name = "UserBasedDiscretization"
class = "com.rapidminer.operator.preprocessing.discretization.UserBasedDiscretization"
description = "Discretize numerical attributes into user defined bins."
group = "Preprocessing.Data.Discretization"/>
<operator
name = "FrequencyDiscretization"
class = "com.rapidminer.operator.preprocessing.discretization.FrequencyDiscretization"
description = "Discretize numerical attributes into a user defined number of bins with equal frequency."
group = "Preprocessing.Data.Discretization"/>
<operator
name = "AbsoluteDiscretization"
class = "com.rapidminer.operator.preprocessing.discretization.AbsoluteDiscretization"
description = "Discretize numerical attributes into bins with user defined number of contained examples."
group = "Preprocessing.Data.Discretization"/>
<operator
name = "MinimalEntropyPartitioning"
class = "com.rapidminer.operator.preprocessing.discretization.MinimalEntropyDiscretization"
description = "Discretizes numerical attributes. Bin boundaries are chosen as to minimize the entropy in the induced partitions."
group = "Preprocessing.Data.Discretization"/>
<operator
name = "ExampleFilter"
class = "com.rapidminer.operator.preprocessing.filter.ExampleFilter"
description = "This operator only allows examples which fulfill a specified condition."
group = "Preprocessing.Data.Filter"/>
<operator
name = "AttributeFilter"
class = "com.rapidminer.operator.preprocessing.filter.attributes.AttributeFilter"
description = "This operator removes attributes which fulfill a specified condition."
group = "Preprocessing.Attributes.Filter"/>
<operator
name = "SetData"
class = "com.rapidminer.operator.preprocessing.filter.SetData"
description = "Sets the data of a specified example and attribute to the specified value."
group = "Preprocessing.Data.Filter"/>
<operator
name = "DateAdjust"
class = "com.rapidminer.operator.preprocessing.filter.DateAdjust"
description = "Adjusts the date in a specified column by adding or subtracting the specified amount of time."
group = "Preprocessing.Data.Filter"/>
<operator
name = "RemoveDuplicates"
class = "com.rapidminer.operator.preprocessing.filter.RemoveDuplicates"
description = "Removes those examples from the given example set which are duplicates to others based on the given attribute specification."
group = "Preprocessing.Data.Filter"/>
<operator
name = "TFIDFFilter"
class = "com.rapidminer.operator.preprocessing.filter.TFIDFFilter"
description = "Performs a TF-IDF filtering to the input data set."
group = "Preprocessing.Data.Filter"/>
<operator
name = "Sorting"
class = "com.rapidminer.operator.preprocessing.filter.Sorting"
description = "This operator sorts the given example set according to a single attribute."
group = "Preprocessing.Data.Filter"/>
<operator
name = "NonDominatedSorting"
class = "com.rapidminer.operator.preprocessing.filter.NonDominatedSorting"
description = "This operator sorts the given example set according a set of attributes such that dominated examples will be sorted after non-dominated examples."
group = "Preprocessing.Data.Filter"/>
<operator
name = "ExampleRangeFilter"
class = "com.rapidminer.operator.preprocessing.filter.ExampleRangeFilter"
description = "This only allows examples in the specified index range."
group = "Preprocessing.Data.Filter"/>
<operator
name = "Permutation"
class = "com.rapidminer.operator.preprocessing.filter.PermutationOperator"
description = "Permutates the examples in the table. Caution: will increase memory usage!"
group = "Preprocessing.Data.Filter"/>
<operator
name = "FeatureGeneration"
class = "com.rapidminer.operator.features.construction.FeatureGenerationOperator"
description = "The feature generation operator generates new user defined features."
deprecation = "Please use the operator 'AttributeConstruction' instead."
group = "Preprocessing.Attributes.Generation"/>
<operator
name = "AttributeConstruction"
class = "com.rapidminer.operator.features.construction.AttributeConstruction"
description = "This operator constructs new user defined attributes from mathematical expressions."
group = "Preprocessing.Attributes.Generation"/>
<operator
name = "ConditionedFeatureGeneration"
class = "com.rapidminer.operator.features.construction.ConditionedFeatureGeneration"
description = "The conditioned feature generation operator allows to generate features with values dependent on conditions."
group = "Preprocessing.Attributes.Generation"/>
<operator
name = "AttributeAggregation"
class = "com.rapidminer.operator.features.construction.AttributeAggregationOperator"
description = "This operator constructs a new attribute by aggregating values of other attributes in every example."
group = "Preprocessing.Attributes.Generation"/>
<operator
name = "GaussAttributeGeneration"
class = "com.rapidminer.operator.features.construction.GaussFeatureConstructionOperator"
description = "Creates a gaussian function based on a given attribute and a specified mean and standard deviation sigma."
group = "Preprocessing.Attributes.Generation"/>
<operator
name = "ProductAttributeGeneration"
class = "com.rapidminer.operator.features.construction.ProductGenerationOperator"
description = "Creates all products based on the attributes specified by regular expressions."
group = "Preprocessing.Attributes.Generation"/>
<operator
name = "LinearCombination"
class = "com.rapidminer.operator.features.construction.LinearCombinationOperator"
description = "This operator created a new example set containing only one feature: the linear combination of all input attributes."
deprecation = "Please use the operator 'AttributeAggregation' instead."
group = "Preprocessing.Attributes.Generation"/>
<operator
name = "CompleteFeatureGeneration"
class = "com.rapidminer.operator.features.construction.CompleteFeatureGenerationOperator"
description = "The feature generation operator generates new features via applying a set of functions on all features."
group = "Preprocessing.Attributes.Generation"/>
<operator
name = "FourierTransform"
class = "com.rapidminer.operator.features.transformation.FourierTransform"
description = "Uses the label as function of each attribute and calculates the fourier transformations as new attributes."
group = "Preprocessing.Attributes.Transformation"/>
<operator
name = "FeatureValueTypeFilter"
class = "com.rapidminer.operator.preprocessing.filter.FeatureValueTypeFilter"
description = "This operator switches off those features whose value type matches the given one."
group = "Preprocessing.Attributes.Filter"/>
<operator
name = "FeatureBlockTypeFilter"
class = "com.rapidminer.operator.preprocessing.filter.FeatureBlockTypeFilter"
description = "This operator switches off those features whose block type matches the given one."
group = "Preprocessing.Attributes.Filter"/>
<operator
name = "FeatureNameFilter"
class = "com.rapidminer.operator.preprocessing.filter.FeatureNameFilter"
description = "This operator switches off those features whose name matches the given one (regular expressions are also allowed)."
group = "Preprocessing.Attributes.Filter"/>
<operator
name = "FeatureRangeRemoval"
class = "com.rapidminer.operator.preprocessing.filter.FeatureRangeRemoval"
description = "This operator removes a range of features."
group = "Preprocessing.Attributes.Filter"/>
<operator
name = "InfiniteValueReplenishment"
class = "com.rapidminer.operator.preprocessing.filter.InfiniteValueReplenishment"
description = "Replaces infinite values in examples."
group = "Preprocessing.Data.Filter"/>
<operator
name = "Partition"
class = "com.rapidminer.operator.preprocessing.sampling.PartitionOperator"
description = "Partitions an example set into subsets according to the specified relative sizes."
group = "Preprocessing.Data.Sampling"/>
<operator
name = "Sampling"
class = "com.rapidminer.operator.preprocessing.sampling.SimpleSampling"
description = "Creates a sample from an example set by drawing a fraction."
group = "Preprocessing.Data.Sampling"/>
<operator
name = "KennardStoneSampling"
class = "com.rapidminer.operator.preprocessing.sampling.KennardStoneSampling"
description = "Creates a sample from an example set using the Kennard-Stone algorithm."
group = "Preprocessing.Data.Sampling"/>
<operator
name = "Bootstrapping"
class = "com.rapidminer.operator.preprocessing.sampling.Bootstrapping"
description = "Creates a bootstrapped sample by sampling with replacement."
group = "Preprocessing.Data.Sampling"/>
<operator
name = "WeightedBootstrapping"
class = "com.rapidminer.operator.preprocessing.sampling.WeightedBootstrapping"
description = "Creates a bootstrapped sample by weighted sampling with replacement."
group = "Preprocessing.Data.Sampling"/>
<operator
name = "AbsoluteSampling"
class = "com.rapidminer.operator.preprocessing.sampling.AbsoluteSampling"
description = "Creates a sample from an example set by drawing an exact number of examples."
group = "Preprocessing.Data.Sampling"/>
<operator
name = "StratifiedSampling"
class = "com.rapidminer.operator.preprocessing.sampling.RatioStratifiedSampling"
description = "Creates a stratified sample from an example set by drawing a fraction."
group = "Preprocessing.Data.Sampling"/>
<operator
name = "AbsoluteStratifiedSampling"
class = "com.rapidminer.operator.preprocessing.sampling.AbsoluteStratifiedSampling"
description = "Creates a stratified sample from an example set by drawing a given number of examples."
group = "Preprocessing.Data.Sampling"/>
<operator
name = "ModelBasedSampling"
class = "com.rapidminer.operator.preprocessing.sampling.ModelBasedSampling"
description = "Creates a sample from an example set. The sampling is based on a model and is constructed to focus on examples not yet explained."
group = "Preprocessing.Data.Sampling"/>
<operator
name = "MissingValueReplenishment"
class = "com.rapidminer.operator.preprocessing.filter.MissingValueReplenishment"
description = "Replaces missing values in examples."
group = "Preprocessing.Data.Filter"/>
<operator
name = "MissingValueReplenishmentView"
class = "com.rapidminer.operator.preprocessing.filter.MissingValueReplenishmentView"
description = "Replaces missing values in examples. In contrast to the usual missing value replenishment, this operator does not change the underlying data but replaces the missing values on the fly by using a new view on the data."
group = "Preprocessing.Data.Filter"/>
<operator
name = "MissingValueImputation"
class = "com.rapidminer.operator.preprocessing.filter.MissingValueImputation"
description = "Replaces missing values in examples by applying a model learned for missing values."
group = "Preprocessing.Data.Filter"/>
<operator
name = "EqualLabelWeighting"
class = "com.rapidminer.operator.preprocessing.weighting.EqualLabelWeighting"
description = "Distributes weight over examples, so that per label weights sum up equally."
group = "Preprocessing.Data.Weighting"/>
<operator
name = "Normalization"
class = "com.rapidminer.operator.preprocessing.normalization.Normalization"
description = "Normalizes the attribute values for a specified range."
group = "Preprocessing"/>
<operator
name = "Nominal2Binary"
class = "com.rapidminer.operator.preprocessing.filter.NominalToBinominal"
description = "Maps all nominal values to binary attributes."
deprecation = "Please use the operator 'Nominal2Binominal' instead."
group = "Preprocessing.Attributes.Filter.Converter"/>
<operator
name = "Nominal2Binominal"
class = "com.rapidminer.operator.preprocessing.filter.NominalToBinominal"
description = "Maps all nominal values to binominal (binary) attributes."
group = "Preprocessing.Attributes.Filter.Converter"/>
<operator
name = "RemoveUselessAttributes"
class = "com.rapidminer.operator.features.selection.RemoveUselessFeatures"
description = "Remove all useless attributes from an example set."
group = "Preprocessing.Attributes.Selection"/>
<operator
name = "NoiseGenerator"
class = "com.rapidminer.operator.preprocessing.NoiseOperator"
description = "Adds noise to existing attributes or add random attributes."
group = "Preprocessing.Other"/>
<operator
name = "Obfuscator"
class = "com.rapidminer.operator.preprocessing.Obfuscator"
description = "Replaces all nominal values and attribute names by random strings."
group = "Preprocessing.Other"/>
<operator
name = "DeObfuscator"
class = "com.rapidminer.operator.preprocessing.Deobfuscator"
description = "Replaces all obfuscated values and attribute names by the ones given in a file."
group = "Preprocessing.Other"/>
<operator
name = "ExampleSetJoin"
class = "com.rapidminer.operator.preprocessing.join.ExampleSetJoin"
description = "Build the join of two example sets using the id attributes of the sets in order to identify the same examples."
group = "Preprocessing.Join"/>
<operator
name = "ExampleSetSuperset"
class = "com.rapidminer.operator.preprocessing.join.ExampleSetSuperset"
description = "This operator gets two example sets and adds new features to each of both example sets so that both example sets consist of the same set of features."
group = "Preprocessing.Join"/>
<operator
name = "ExampleSetUnion"
class = "com.rapidminer.operator.preprocessing.join.ExampleSetUnion"
description = "This operator first builds the union set / superset for both input example sets and merges both extended sets into a new one."
group = "Preprocessing.Join"/>
<operator
name = "ExampleSetMinus"
class = "com.rapidminer.operator.preprocessing.join.ExampleSetMinus"
description = "This operator returns these examples of an example set whose IDs are not contained within another example set."
group = "Preprocessing.Join"/>
<operator
name = "ExampleSetIntersect"
class = "com.rapidminer.operator.preprocessing.join.ExampleSetIntersect"
description = "This operator returns these examples of an example set whose IDs are contained within another example set."
group = "Preprocessing.Join"/>
<operator
name = "ExampleSetCartesian"
class = "com.rapidminer.operator.preprocessing.join.ExampleSetCartesian"
description = "Build the cartesian product of two example sets. In contrast to the ExampleSetJoin operator Id attributes are not needes."
group = "Preprocessing.Join"/>
<operator
name = "ExampleSetMerge"
class = "com.rapidminer.operator.preprocessing.join.ExampleSetMerge"
description = "Build a merged example set from two or more compatible example sets by adding all examples into a combined set."
group = "Preprocessing.Join"/>
<operator
name = "ExampleSetTranspose"
class = "com.rapidminer.operator.preprocessing.ExampleSetTranspose"
description = "Transposes the input example set similar to the matrix operator transpose."
group = "Preprocessing.Other"/>
<operator
name = "UseRowAsAttributeNames"
class = "com.rapidminer.operator.preprocessing.UseRowAsAttributeNames"
description = "Uses the specified row as new attibute names and deletes the row from the data set."
group = "Preprocessing.Other"/>
<operator
name = "PCA"
class = "com.rapidminer.operator.features.transformation.PCA"
description = "Performs a principal component analysis (PCA) using the covariance matrix."
group = "Preprocessing.Attributes.Transformation"/>
<operator
name = "KernelPCA"
class = "com.rapidminer.operator.features.transformation.KernelPCA"
description = "Performs a kernel principal component analysis (PCA)."
group = "Preprocessing.Attributes.Transformation"/>
<operator
name = "SVDReduction"
class = "com.rapidminer.operator.features.transformation.SVDReduction"
description = "Performs a dimensionality reduction based on Singular Value Decomposition (SVD)."
group = "Preprocessing.Attributes.Transformation"/>
<operator
name = "SOMDimensionalityReduction"
class = "com.rapidminer.operator.features.transformation.SOMDimensionalityReduction"
description = "Trains a self-organizing map and applyes the examples on the map. The resulting coordinates are used as new attributes."
group = "Preprocessing.Attributes.Transformation"/>
<operator
name = "GHA"
class = "com.rapidminer.operator.features.transformation.GHA"
description = "Generalized Hebbian Algorithm (GHA). Performs an iterative principal components analysis."
group = "Preprocessing.Attributes.Transformation"/>
<operator
name = "FastICA"
class = "com.rapidminer.operator.features.transformation.FastICA"
description = "Performs an independent component analysis (ICA)."
group = "Preprocessing.Attributes.Transformation"/>
<operator
name = "ComponentWeights"
class = "com.rapidminer.operator.features.weighting.ComponentWeights"
description = "Creates the AttributeWeights of models containing components like PCA, GHA or FastICA."
group = "Preprocessing.Attributes.Weighting"/>
<operator
name = "RemoveCorrelatedFeatures"
class = "com.rapidminer.operator.features.selection.RemoveCorrelatedFeatures"
description = "Removes correlated features."
group = "Preprocessing.Attributes.Selection"/>
<operator
name = "DensityBasedOutlierDetection"
class = "com.rapidminer.operator.preprocessing.outlier.DBOutlierOperator"
description = "Identifies outliers in the given ExampleSet based on the data density."
group = "Preprocessing.Data.Outlier"/>
<operator
name = "LOFOutlierDetection"
class = "com.rapidminer.operator.preprocessing.outlier.LOFOutlierOperator"
description = "Identifies outliers in the given ExampleSet based on local outlier factors."
group = "Preprocessing.Data.Outlier"/>
<operator
name = "DistanceBasedOutlierDetection"
class = "com.rapidminer.operator.preprocessing.outlier.DKNOutlierOperator"
description = "Identifies n outliers in the given ExampleSet based on the distance to their k nearest neighbors."
group = "Preprocessing.Data.Outlier"/>
<!-- Postprocessing Operators -->
<operator
name = "FrequentItemSetUnificator"
class = "com.rapidminer.operator.learner.associations.FrequentItemSetUnificator"
description = "Compares sets of frequent item sets and removes common not unique sets."
group = "Postprocessing"/>
<operator
name = "PlattScaling"
class = "com.rapidminer.operator.postprocessing.PlattScaling"
description = "Turns confidence scores of boolean classifiers into probability estimates."
group = "Postprocessing"/>
<operator
name = "ThresholdFinder"
class = "com.rapidminer.operator.postprocessing.ThresholdFinder"
description = "Finds a threshold for given prediction confidences (soft predictions) , costs and distributional information in order to turn it into a crisp classification. The optimization step is based on ROC analysis."
group = "Postprocessing"/>
<operator
name = "ThresholdCreator"
class = "com.rapidminer.operator.postprocessing.ThresholdCreator"
description = "Creates a user defined threshold for given prediction confidences (soft predictions) in order to turn it into a crisp classifier."
group = "Postprocessing"/>
<operator
name = "ThresholdApplier"
class = "com.rapidminer.operator.postprocessing.ThresholdApplier"
description = "Applies a threshold on soft classified data."
group = "Postprocessing"/>
<operator
name = "UncertainPredictionsTransformation"
class = "com.rapidminer.operator.postprocessing.SimpleUncertainPredictionsTransformation"
description = "Sets all predictions to 'unknown' (missing value) if the corresponding confidence is smaller than the specified value."
group = "Postprocessing"/>
<operator
name = "WindowExamples2OriginalData"
class = "com.rapidminer.operator.postprocessing.WindowExamples2OriginalData"
description = "Transform a data set transformed with a multivariate windowing followed by a WindowExamples2ModelingData operator into a data set where both the label and the predicted label (if applicable, i.e. after a ModelApplier) are transformed into their original data ranges by adding the values of the base value attribute."
group = "Postprocessing"/>
<!-- Meta Operators -->
<operator
name = "ProcessEmbedder"
class = "com.rapidminer.operator.meta.ProcessEmbeddingOperator"
description = "This operator embeds a complete process previously written into a file."
group = "Meta"/>
<operator
name = "ExperimentEmbedder"
class = "com.rapidminer.operator.meta.ProcessEmbeddingOperator"
description = "This operator embeds a complete experiment previously written into a file."
deprecation = "Please use the operator 'ProcessEmbedder' instead (and note the change of the parameter name!)"
group = "Meta"/>
<operator
name = "DirectoryIterator"
class = "com.rapidminer.operator.meta.FileIterator"
description = "This operator iterates over all files in a directory and delivers their paths and names as macro to the inner operators."
deprecation = "Please use the operator 'FileIterator' instead."
group = "Meta.Other"/>
<operator
name = "FileIterator"
class = "com.rapidminer.operator.meta.FileIterator"
description = "This operator iterates over all files in a directory and delivers their paths and names as macro to the inner operators."
group = "Meta.Other"/>
<operator
name = "BatchProcessing"
class = "com.rapidminer.operator.meta.BatchProcessing"
description = "Creates batches from the input examples and performs its inner operators on each of these batches which might be useful for applying methods on very large data sets directly in databases."
group = "Meta"/>
<operator
name = "SplitChain"
class = "com.rapidminer.operator.meta.RatioSplitChain"
description = "Splits an example set in two parts based on a user defined ratio and uses the output of the first child and the second part as input for the second child."
group = "Meta.Control"/>
<operator
name = "AbsoluteSplitChain"
class = "com.rapidminer.operator.meta.AbsoluteSplitChain"
description = "Splits an example set in two parts based on user defined set sizes and uses the output of the first child and the second part as input for the second child."
group = "Meta.Control"/>
<operator
name = "OperatorEnabler"
class = "com.rapidminer.operator.meta.OperatorEnabler"
description = "This operator can be used to automatically enable or disable inner operators."
group = "Meta.Control"/>
<operator
name = "OperatorSelector"
class = "com.rapidminer.operator.meta.OperatorSelector"
description = "This operator can be used to select a single inner operator which should be performed, e.g. by means of parameter iteration or optimization operators."
group = "Meta.Control"/>
<operator
name = "ProcessBranch"
class = "com.rapidminer.operator.meta.branch.ProcessBranch"
description = "This operator provides a conditional execution of operators."
group = "Meta.Control"/>
<operator
name = "ErrorNeglector"
class = "com.rapidminer.operator.meta.ExceptionHandling"
description = "This operator performs the inner operator and neglects any errors. In this case, no inner output will be returned."
deprecation = "Please use the operator 'ExceptionHandling' instead."
group = "Meta.Other"/>
<operator
name = "ExceptionHandling"
class = "com.rapidminer.operator.meta.ExceptionHandling"
description = "This operator performs the inner operator and neglects any errors. In this case, no inner output will be returned."
group = "Meta.Other"/>
<operator
name = "LearningCurve"
class = "com.rapidminer.operator.meta.LearningCurveOperator"
description = "Iterates its inner operator for an increasing number of samples and collects the performances."
group = "Meta.Other"/>
<operator
name = "PartialExampleSetLearner"
class = "com.rapidminer.operator.meta.PartialExampleSetLearner"
description = "Uses only a fraction of the data to apply the inner operator on it."
group = "Meta.Other"/>
<operator
name = "ExampleSetIterator"
class = "com.rapidminer.operator.meta.ExampleSetIterator"
description = "Performs its inner operators for each example set found in input."
group = "Meta.Control"/>
<operator
name = "MultipleLabelIterator"
class = "com.rapidminer.operator.meta.MultipleLabelIterator"
description = "Performs its inner operators for each label found in input example set."
group = "Meta.Other"/>
<operator
name = "RepeatUntilOperatorChain"
class = "com.rapidminer.operator.meta.RepeatUntilOperatorChain"
description = "Performs its inner operators until some condition is met."
group = "Meta.Control"/>
<operator
name = "IteratingOperatorChain"
class = "com.rapidminer.operator.meta.IteratingOperatorChain"
description = "Performs its inner operators k times."
group = "Meta.Control"/>
<operator
name = "FeatureSubsetIteration"
class = "com.rapidminer.operator.meta.FeatureSubsetIteration"
description = "Performs its inner operator for all specified feature subsets (useful for brute force evaluations in combination with the ProcessLog operator)."
group = "Meta.Control"/>
<operator
name = "FeatureIterator"
class = "com.rapidminer.operator.meta.FeatureIterator"
description = "Iterates over the given features and applies the inner operators for each feature where the inner operators can access the current feature name by a macro."
group = "Meta.Control"/>
<operator
name = "ValueSubgroupIterator"
class = "com.rapidminer.operator.meta.ValueSubgroupIteration"
description = "Iterates over the values of the specified attributes and applies the inner operators on the subgroups which exhibit the current attribute value."
group = "Meta.Control"/>
<operator
name = "ValueIterator"
class = "com.rapidminer.operator.meta.ValueIteration"
description = "Iterates over the values of the specified attributes and applies the inner operators on the input example set while the current value can be accessed via a macro. In contrast to the ValueSubgroupIterator operator the inner operators are applied on the complete example set."
group = "Meta.Control"/>
<operator
name = "RandomOptimizer"
class = "com.rapidminer.operator.meta.RandomOptimizationChain"
description = "Performs its inner operators k times and returns the best results."
group = "Meta.Other"/>
<operator
name = "ParameterSetter"
class = "com.rapidminer.operator.meta.ParameterSetter"
description = "Applies a set of parameters. Operator names may be remapped."
group = "Meta.Parameter"/>
<operator
name = "ParameterCloner"
class = "com.rapidminer.operator.meta.ParameterCloner"
description = "Applies a set of parameters of a source operator on a target operator."
group = "Meta.Parameter"/>
<operator
name = "GridParameterOptimization"
class = "com.rapidminer.operator.meta.GridSearchParameterOptimizationOperator"
description = "This operator finds the optimal values for parameters."
group = "Meta.Parameter"/>
<operator
name = "QuadraticParameterOptimization"
class = "com.rapidminer.operator.meta.QuadraticParameterOptimizationOperator"
description = "This operator finds the optimal values for parameters using a quadratic interaction model."
group = "Meta.Parameter"/>
<operator
name = "EvolutionaryParameterOptimization"
class = "com.rapidminer.operator.meta.EvolutionaryParameterOptimizationOperator"
description = "This operator finds the optimal values for parameters using an evolutionary computation approach."
group = "Meta.Parameter"/>
<operator
name = "ParameterIteration"
class = "com.rapidminer.operator.meta.ParameterIteration"
description = "This operator just iterates through all defined parameter combinations."
group = "Meta.Parameter"/>
<operator
name = "AverageBuilder"
class = "com.rapidminer.operator.meta.AverageBuilder"
description = "Builds the average of input average vectors (e.g. performance) of the same type."
group = "Meta.Other"/>
<operator
name = "XVPrediction"
class = "com.rapidminer.operator.meta.XVPrediction"
description = "Predicts the examples in a cross-validation-like fashion."
group = "Meta.Control"/>
<operator
name = "ClusterIteration"
class = "com.rapidminer.operator.meta.ClusterIterator"
description = "Applies all inner operators to all clusters."
group = "Meta.Control"/>
<operator
name = "SeriesPrediction"
class = "com.rapidminer.operator.meta.UnivariateLabelSeriesPrediction"
description = "Creates predictions for an ordered time series given as the label attribute of an example set."
group = "Meta.Other"/>
<!-- Visualization Operators -->
<operator
name = "ProcessLog"
class = "com.rapidminer.operator.visualization.ProcessLogOperator"
description = "Saves almost arbitrary data to a log table (also possibly in a file) and create statistics for online plotting of values/parameters provided by operators."
group = "Visualization"/>
<operator
name = "ClearProcessLog"
class = "com.rapidminer.operator.visualization.ClearProcessLog"
description = "Clears a table generated by a ProcessLog operator."
group = "Visualization.Logging"/>
<operator
name = "ExperimentLog"
class = "com.rapidminer.operator.visualization.ProcessLogOperator"
description = "Saves almost arbitrary data to a log file and create statistics for online plotting of values/parameters provided by operators."
deprecation = "Please use the operator 'ProcessLog' instead."
group = "Visualization.Logging"/>
<operator
name = "ProcessLog2ExampleSet"
class = "com.rapidminer.operator.visualization.ProcessLog2ExampleSet"
description = "Transforms the data generated by the ProcessLog operator into an example set which can be used by other operators."
group = "Visualization.Logging"/>
<operator
name = "Data2Log"
class = "com.rapidminer.operator.visualization.Data2Log"
description = "Reads the specified value from the input example set and provides the value for logging purposes."
group = "Visualization.Logging"/>
<operator
name = "Macro2Log"
class = "com.rapidminer.operator.visualization.Macro2Log"
description = "Reads the value from the specified macro and provides the value for logging purposes."
group = "Visualization.Logging"/>
<operator
name = "ExampleVisualizer"
class = "com.rapidminer.operator.visualization.ExampleVisualizationOperator"
description = "Allows the visualization of examples (attribute values) in the plot view of an example set (double click on data point)."
group = "Visualization"/>
<operator
name = "DatabaseExampleVisualizer"
class = "com.rapidminer.operator.visualization.DatabaseExampleVisualizationOperator"
description = "Allows the visualization of examples (attribute values) in the plot view of an example set (double click on data point). The data is directly derived from the specified database table."
group = "Visualization"/>
<operator
name = "DataStatistics"
class = "com.rapidminer.operator.visualization.DataStatisticsOperator"
description = "Calculates some simple data statistics usually displayed by the GUI (only necessary for command line processes)."
group = "Visualization"/>
<operator
name = "ANOVAMatrix"
class = "com.rapidminer.operator.visualization.dependencies.ANOVAMatrixOperator"
description = "Performs an ANOVA significance test for a all numerical attribute based on the groups defined by all other nominal attributes."
group = "Visualization.Dependencies"/>
<operator
name = "CorrelationMatrix"
class = "com.rapidminer.operator.visualization.dependencies.CorrelationMatrixOperator"
description = "Determines the correlation between all attributes and can produce a weight vector based on correlations."
group = "Visualization.Dependencies"/>
<operator
name = "CovarianceMatrix"
class = "com.rapidminer.operator.visualization.dependencies.CovarianceMatrixOperator"
description = "Determines the covariance between all attributes."
group = "Visualization.Dependencies"/>
<operator
name = "TransitionMatrix"
class = "com.rapidminer.operator.visualization.dependencies.TransitionMatrixOperator"
description = "Determines the transition probabilities of nominal values."
group = "Visualization.Dependencies"/>
<operator
name = "TransitionGraph"
class = "com.rapidminer.operator.visualization.dependencies.TransitionGraphOperator"
description = "Creates a graphical representation of transitions defined by source and target attributes."
group = "Visualization.Dependencies"/>
<operator
name = "MutualInformationMatrix"
class = "com.rapidminer.operator.visualization.dependencies.MutualInformationMatrixOperator"
description = "Determines the mutual information between all attributes."
group = "Visualization.Dependencies"/>
<operator
name = "RainflowMatrix"
class = "com.rapidminer.operator.visualization.dependencies.RainflowMatrixOperator"
description = "Determines the rainflow matrix for a specified attribute."
group = "Visualization.Series"/>
<operator
name = "LiftChart"
class = "com.rapidminer.operator.visualization.LiftChartGenerator"
description = "Generates a lift chart for the given binominal model and input data set."
group = "Visualization"/>
<operator
name = "LiftParetoChart"
class = "com.rapidminer.operator.visualization.LiftParetoChartGenerator"
description = "Generates a lift chart for the given model and input data set based on discretized confidences and a Pareto chart."
group = "Visualization"/>
<operator
name = "ROCChart"
class = "com.rapidminer.operator.visualization.ROCChartGenerator"
description = "Generates a ROC chart for the given binominal model and input data set."
group = "Visualization"/>
<operator
name = "ROCComparator"
class = "com.rapidminer.operator.visualization.ROCBasedComparisonOperator"
description = "Generates a ROC chart for the models created by each of the inner learners and plot all charts in the same plotter."
group = "Visualization"/>
<operator
name = "ModelVisualizer"
class = "com.rapidminer.operator.visualization.SOMModelVisualization"
description = "Generates a SOM plot (transforming arbitrary number of dimensions to two) of the given data set and colorizes the landscape with the predictions of the given model."
group = "Visualization"/>
<operator
name = "FormulaExtractor"
class = "com.rapidminer.operator.visualization.FormulaExtractor"
description = "Generates a formula from a given model for models which are capable of producing formulas."
group = "Visualization"/>
<!-- OLAP Operators -->
<operator
name = "GroupedANOVA"
class = "com.rapidminer.operator.preprocessing.transformation.GroupedANOVAOperator"
description = "Performs an ANOVA significance test for a single numerical attribute based on the groups defined by another (nominal) attribute."
group = "OLAP"/>
<operator
name = "Aggregation"
class = "com.rapidminer.operator.preprocessing.transformation.AggregationOperator"
description = "Performs one of the aggregation functions (count, sum...) known from SQL (allows also grouping)."
group = "OLAP"/>
<operator
name = "Example2AttributePivoting"
class = "com.rapidminer.operator.preprocessing.transformation.Example2AttributePivoting"
description = "Transforms an example set by grouping multiple examples of single units to single examples."
group = "OLAP"/>
<operator
name = "Attribute2ExamplePivoting"
class = "com.rapidminer.operator.preprocessing.transformation.Attribute2ExamplePivoting"
description = "Transforms an example set by dividing examples containing multiple observations (in attributes) into multiple examples."
group = "OLAP"/>
<operator
name = "GroupBy"
class = "com.rapidminer.operator.preprocessing.GroupByOperator"
description = "Partitions an example set according to the values of a single nominal or integer attributes."
group = "OLAP"/>
<!-- Data generation Operators -->
<operator
name = "ExampleSetGenerator"
class = "com.rapidminer.operator.generator.ExampleSetGenerator"
description = "Generates an example set based on numerical attributes."
group = "IO.Generator"/>
<operator
name = "NominalExampleSetGenerator"
class = "com.rapidminer.operator.generator.NominalExampleSetGenerator"
description = "Generates an example set based on nominal attributes."
group = "IO.Generator"/>
<operator
name = "MassiveDataGenerator"
class = "com.rapidminer.operator.generator.MassiveDataGenerator"
description = "Generates huge amounts of data for testing purposes."
group = "IO.Generator"/>
<operator
name = "DirectMailingExampleSetGenerator"
class = "com.rapidminer.operator.generator.DirectMailingExampleSetGenerator"
description = "Generates data for testing purposes based on a direct mailing data set."
group = "IO.Generator"/>
<operator
name = "UpSellingExampleSetGenerator"
class = "com.rapidminer.operator.generator.UpSellingExampleSetGenerator"
description = "Generates data for testing purposes based on an up-selling data set."
group = "IO.Generator"/>
<operator
name = "ChurnReductionExampleSetGenerator"
class = "com.rapidminer.operator.generator.ChurnReductionExampleSetGenerator"
description = "Generates data for testing purposes based on a churn reduction data set."
group = "IO.Generator"/>
<operator
name = "TeamProfitExampleSetGenerator"
class = "com.rapidminer.operator.generator.TeamProfitExampleSetGenerator"
description = "Generates data for testing purposes based on a team profit data set."
group = "IO.Generator"/>
<operator
name = "SalesExampleSetGenerator"
class = "com.rapidminer.operator.generator.SalesExampleSetGenerator"
description = "Generates data for testing purposes based on a sales data set."
group = "IO.Generator"/>
<operator
name = "TransfersExampleSetGenerator"
class = "com.rapidminer.operator.generator.TransfersExampleSetGenerator"
description = "Generates data for testing purposes based on a transfers data set."
group = "IO.Generator"/>
<operator
name = "MultipleLabelGenerator"
class = "com.rapidminer.operator.generator.MultipleLabelGenerator"
description = "Generates an example set based on numerical attributes and with more than one label."
group = "IO.Generator"/>
<!-- Series Operators -->
<operator
name = "Single2Series"
class = "com.rapidminer.operator.preprocessing.series.SingleAttributes2ValueSeries"
description = "Changes the value type of all single valued attributes and forms a value series from all attributes."
group = "Preprocessing.Series"/>
<operator
name = "Series2WindowExamples"
class = "com.rapidminer.operator.preprocessing.series.UnivariateSeries2WindowExamples"
description = "Creates examples from an univariate value series data by windowing and using a label value with respect to a user defined prediction horizon."
group = "Preprocessing.Series"/>
<operator
name = "MultivariateSeries2WindowExamples"
class = "com.rapidminer.operator.preprocessing.series.MultivariateSeries2WindowExamples"
description = "Creates examples from a multivariate value series data set by windowing the input data."
group = "Preprocessing.Series"/>
<operator
name = "FillDataGaps"
class = "com.rapidminer.operator.preprocessing.series.FillDataGaps"
description = "This operator fills gaps in the data based on the ID attribute of the data set."
group = "Preprocessing.Series"/>
<operator
name = "WindowExamples2ModelingData"
class = "com.rapidminer.operator.preprocessing.series.WindowExamples2ModelingData"
description = "Transform the result of a multivariate windowing into a data set which can be used for training / model application by removing all attributes in the horizon, defining the label, transforming the data into a relative format."
group = "Preprocessing.Series"/>
<operator
name = "LabelTrend2Classification"
class = "com.rapidminer.operator.preprocessing.series.LabelTrend2Classification"
description = "This operator iterates over an example set with numeric label and converts the label values to either the class 'up' or the class 'down' based on whether the change from the previous label is positive or negative."
group = "Preprocessing.Series"/>
<operator
name = "LagSeries"
class = "com.rapidminer.operator.preprocessing.series.filter.LagSeries"
description = "Lags a specified series."
group = "Preprocessing.Series.Filter"/>
<operator
name = "DifferentiateSeries"
class = "com.rapidminer.operator.preprocessing.series.filter.DifferentiateSeries"
description = "Extracts changes between subsequent series values."
group = "Preprocessing.Series.Filter"/>
<operator
name = "CumulateSeries"
class = "com.rapidminer.operator.preprocessing.series.filter.CumulateSeries"
description = "Generates the cumulative sum series."
group = "Preprocessing.Series.Filter"/>
<operator
name = "IndexSeries"
class = "com.rapidminer.operator.preprocessing.series.filter.IndexSeries"
description = "Calculates an index series from an origional series."
group = "Preprocessing.Series.Filter"/>
<operator
name = "MovingAverage"
class = "com.rapidminer.operator.preprocessing.series.filter.MovingAverage"
description = "Generates a new attribute containing the moving average series of a series attribute."
group = "Preprocessing.Series.Filter"/>
<operator
name = "ExponentialSmoothing"
class = "com.rapidminer.operator.preprocessing.series.filter.ExponentialSmoothing"
description = "Generates a new attribute which smoothes a given attribute."
group = "Preprocessing.Series.Filter"/>
<operator
name = "Trend"
class = "com.rapidminer.operator.preprocessing.series.filter.Trend"
description = "Adds a trend for an attribute which is generated by an inner regression lerner."
group = "Preprocessing.Series.Filter"/>
<operator
name = "SeriesMissingValueReplenishment"
class = "com.rapidminer.operator.preprocessing.series.filter.SeriesMissingValueReplenishment"
description = "Replaces missing value in a time series attribute."
group = "Preprocessing.Series.Filter"/>
<operator
name = "EnsureMonotonicty"
class = "com.rapidminer.operator.preprocessing.series.EnsureMonotonicity"
description = "Ensures that all values of a specified attribute are monotonically increasing."
group = "Preprocessing.Series.Filter"/>
<!-- Text Operators -->
<operator
name = "TextObjectWriter"
class = "com.rapidminer.operator.text.TextObjectWriter"
description = "Writes a textobject into a file."
group = "IO.Text.TextObject" />
<operator
name = "TextObjectLoader"
class = "com.rapidminer.operator.text.TextObjectLoader"
description = "Loads a textobject from file."
group = "IO.Text.TextObject" />
<operator
name = "TextExtractor"
class = "com.rapidminer.operator.text.TextExtractor"
description = "Extracts the text of a textobject that matchs a given regular expression and returns it."
group = "IO.Text.TextObject" />
<operator
name = "SingleTextObjectInput"
class = "com.rapidminer.operator.text.SingleTextObjectInput"
description = "Generates a textObject a single text."
group = "IO.Text.TextObject" />
<operator
name = "TextCleaner"
class = "com.rapidminer.operator.text.TextCleaner"
description = "Removes parts of the text matching a given regular expression."
group = "IO.Text.TextObject" />
<operator
name = "TextObject2ExampleSet"
class = "com.rapidminer.operator.text.TextObject2ExampleSet"
description = "Generates an exampleSet containing the text as nominal attribute value."
group = "IO.Text.TextObject" />
<operator
name = "TextSegmenter"
class = "com.rapidminer.operator.text.TextSegmenter"
description = "Splits a Input TextObject into segments using regular expressions specifiying start and end of segments."
group = "IO.Text.TextObject" />
</operators>
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