Uses of Class
com.rapidminer.operator.OperatorChain

Packages that use OperatorChain

com.rapidminer.operator	Provides operators for machine learning and data pre-processing.
com.rapidminer.operator.clustering.clusterer	The operators for clustering.
com.rapidminer.operator.condition	Operator conditions are used to ensure that inner operators of an OperatorChain are correctly embedded.
com.rapidminer.operator.features	Provides feature handling operators.
com.rapidminer.operator.features.aggregation	Provides operators for automatic feature aggregation.
com.rapidminer.operator.features.construction	Provides operators for automatic feature construction.
com.rapidminer.operator.features.selection	Provides operators for automatic feature selection.
com.rapidminer.operator.features.weighting	Operators to weight features or determine feature relevance.
com.rapidminer.operator.learner.meta	Meta learning schemes which uses other learning operators to increase the performance.
com.rapidminer.operator.learner.tree	Provides decision tree learners.
com.rapidminer.operator.learner.weka	Operators which encapsulate the learning schemes provided by Weka.
com.rapidminer.operator.meta	Provides operators for experiment iteration, meta operators, and optimization.
com.rapidminer.operator.meta.branch	Provides operators for conditioned branching.
com.rapidminer.operator.preprocessing	Operators for preprocessing purposes.
com.rapidminer.operator.preprocessing.filter	Containing filter operators changing the input example set, e.g. by removing certain attributes or changing the data.
com.rapidminer.operator.preprocessing.series.filter	Containing preprocessing operators for (time) series filtering.
com.rapidminer.operator.text	This package contains operators and objects to work on texts.
com.rapidminer.operator.validation	Operators for estimation of the performance which can be achieved by learning schemes (and other predictive operators).
com.rapidminer.operator.visualization	The operators in this package are used for visualization purposes.

Uses of OperatorChain in com.rapidminer.operator

Subclasses of OperatorChain in com.rapidminer.operator

class	ProcessRootOperator Each process must contain exactly one operator of this class and it must be the root operator of the process.
class	SimpleOperatorChain A simple operator chain which can have an arbitrary number of inner operators.

Methods in com.rapidminer.operator that return OperatorChain

OperatorChain	IllegalNumberOfInnerOperatorsException.getOperatorChain()
OperatorChain	Operator.getParent() Returns the parent of this operator which must be an operator chain.

Methods in com.rapidminer.operator with parameters of type OperatorChain

void	Operator.setParent(OperatorChain parent) Sets the enclosing operator chain.

Constructors in com.rapidminer.operator with parameters of type OperatorChain

IllegalNumberOfInnerOperatorsException(java.lang.String message, OperatorChain operatorChain)

WrongNumberOfInnerOperatorsException(OperatorChain operator, int min, int max, int actual)

Uses of OperatorChain in com.rapidminer.operator.clustering.clusterer

Subclasses of OperatorChain in com.rapidminer.operator.clustering.clusterer

class

TopDownClustering A top-down generic clustering that can be used with any (flat) clustering as inner operator.

Uses of OperatorChain in com.rapidminer.operator.condition

Methods in com.rapidminer.operator.condition with parameters of type OperatorChain

java.lang.Class[]	SpecificInnerOperatorCondition.checkIO(OperatorChain chain, java.lang.Class[] input)
java.lang.Class[]	SimpleChainInnerOperatorCondition.checkIO(OperatorChain chain, java.lang.Class[] input)
java.lang.Class[]	LastInnerOperatorCondition.checkIO(OperatorChain chain, java.lang.Class[] input)
java.lang.Class[]	InnerOperatorCondition.checkIO(OperatorChain chain, java.lang.Class[] input) Checks if the condition is fulfilled in the given operator chain.
java.lang.Class[]	FirstInnerOperatorCondition.checkIO(OperatorChain chain, java.lang.Class[] input)
java.lang.Class[]	CombinedInnerOperatorCondition.checkIO(OperatorChain chain, java.lang.Class[] input)
java.lang.Class[]	AllInnerOperatorCondition.checkIO(OperatorChain chain, java.lang.Class[] input)

Uses of OperatorChain in com.rapidminer.operator.features

Subclasses of OperatorChain in com.rapidminer.operator.features

class

FeatureOperator This class is the superclass of all feature selection and generation operators.

Uses of OperatorChain in com.rapidminer.operator.features.aggregation

Subclasses of OperatorChain in com.rapidminer.operator.features.aggregation

class

EvolutionaryFeatureAggregation Performs an evolutionary feature aggregation.

Uses of OperatorChain in com.rapidminer.operator.features.construction

Subclasses of OperatorChain in com.rapidminer.operator.features.construction

class	AbstractGeneratingGeneticAlgorithm In contrast to its superclass GeneticAlgorithm, the GeneratingGeneticAlgorithm generates new attributes and thus can change the length of an individual.
class	AGA Basically the same operator as the GeneratingGeneticAlgorithm operator.
class	DirectedGGA DirectedGGA is an acronym for a Generating Genetic Algorithm which uses probability directed search heuristics to select attributes for generation or removing.
class	ExampleSetBasedFeatureOperator This class is the superclass of all feature selection and generation operators.
class	FourierGGA FourierGGA has all functions of YAGGA2.
class	GeneratingGeneticAlgorithm In contrast to the class GeneticAlgorithm, the GeneratingGeneticAlgorithm generates new attributes and thus can change the length of an individual.
class	YAGGA YAGGA is an acronym for Yet Another Generating Genetic Algorithm.
class	YAGGA2 YAGGA is an acronym for Yet Another Generating Genetic Algorithm.

Uses of OperatorChain in com.rapidminer.operator.features.selection

Subclasses of OperatorChain in com.rapidminer.operator.features.selection

class	AbstractGeneticAlgorithm Genetic algorithms are general purpose optimization / search algorithms that are suitable in case of no or little problem knowledge.
class	BruteForceSelection This feature selection operator selects the best attribute set by trying all possible combinations of attribute selections.
class	FeatureSelectionOperator This operator realizes the two deterministic greedy feature selection algorithms forward selection and backward elimination.
class	ForwardSelectionOperator
class	GeneticAlgorithm A genetic algorithm for feature selection (mutation=switch features on and off, crossover=interchange used features).
class	WeightGuidedSelectionOperator This operator uses input attribute weights to determine the order of features added to the feature set starting with the feature set containing only the feature with highest weight.

Uses of OperatorChain in com.rapidminer.operator.features.weighting

Subclasses of OperatorChain in com.rapidminer.operator.features.weighting

class	BackwardWeighting Uses the backward selection idea for the weighting of features.
class	EvolutionaryWeighting This operator performs the weighting of features with an evolutionary strategies approach.
class	FeatureWeighting This operator performs the weighting under the naive assumption that the features are independent from each other.
class	ForwardWeighting This operator performs the weighting under the naive assumption that the features are independent from each other.
class	PSOWeighting This operator performs the weighting of features with a particle swarm approach.

Uses of OperatorChain in com.rapidminer.operator.learner.meta

Subclasses of OperatorChain in com.rapidminer.operator.learner.meta

class	AbstractMetaLearner A MetaLearner is an operator that encapsulates one or more learning steps to build its model.
class	AbstractStacking This class uses n+1 inner learners and generates n different models by using the last n learners.
class	AdaBoost This AdaBoost implementation can be used with all learners available in RapidMiner, not only the ones which originally are part of the Weka package.
class	AdditiveRegression This operator uses regression learner as a base learner.
class	Bagging This Bagging implementation can be used with all learners available in RapidMiner, not only the ones which originally are part of the Weka package.
class	BayBoostStream Assumptions: target label is always boolean goal is to fit a crisp ensemble classifier (use_distribution always off) base classifier weights are always adapted by a single row from first to last no internal bootstrapping
class	BayesianBoosting This operator trains an ensemble of classifiers for boolean target attributes.
class	Binary2MultiClassLearner A metaclassifier for handling multi-class datasets with 2-class classifiers.
class	ClassificationByRegression For a classified dataset (with possibly more than two classes) builds a classifier using a regression method which is specified by the inner operator.
class	CostBasedThresholdLearner This operator uses a set of class weights and also allows a weight for the fact that an example is not classified at all (marked as unknown).
class	MetaCost This operator uses a given cost matrix to compute label predictions according to classification costs.
class	RelativeRegression This meta regression learner transforms the label on-the-fly relative to the value of the specified attribute.
class	SDRulesetInduction Subgroup discovery learner.
class	Stacking This class uses n+1 inner learners and generates n different models by using the last n learners.
class	TransformedRegression This meta learner applies a transformation on the label before the inner regression learner is applied.
class	Tree2RuleConverter This meta learner uses an inner tree learner and creates a rule model from the learned decision tree.
class	Vote This class uses n+1 inner learners and generates n different models by using the last n learners.

Uses of OperatorChain in com.rapidminer.operator.learner.tree

Subclasses of OperatorChain in com.rapidminer.operator.learner.tree

class	MultiwayDecisionTree This operator is a meta learner for numerical tree builder.
class	RelevanceTreeLearner Learns a pruned decision tree based on arbitrary feature relevance measurements defined by an inner operator (use for example InfoGainRatioWeighting for C4.5 and ChiSquaredWeighting for CHAID.

Uses of OperatorChain in com.rapidminer.operator.learner.weka

Subclasses of OperatorChain in com.rapidminer.operator.learner.weka

class	GenericWekaEnsembleLearner Performs the ensemble learning scheme of Weka with the same name.
class	GenericWekaMetaLearner Performs the meta learning scheme of Weka with the same name.

Uses of OperatorChain in com.rapidminer.operator.meta

Subclasses of OperatorChain in com.rapidminer.operator.meta

class	AbsoluteSplitChain An operator chain that split an ExampleSet into two disjunct parts and applies the first child operator on the first part and applies the second child on the second part and the result of the first child.
class	AbstractSplitChain An operator chain that split an ExampleSet into two disjunct parts and applies the first child operator on the first part and applies the second child on the second part and the result of the first child.
class	BatchProcessing This operator groups the input examples into batches of the specified size and performs the inner operators on all batches subsequently.
class	ClusterIterator This operator splits up the input example set according to the clusters and applies its inner operators number_of_clusters time.
class	EvolutionaryParameterOptimizationOperator This operator finds the optimal values for a set of parameters using an evolutionary strategies approach which is often more appropriate than a grid search or a greedy search like the quadratic programming approach and leads to better results.
class	ExampleSetIterator For each example set the ExampleSetIterator finds in its input, the inner operators are applied as if it was an OperatorChain.
class	ExceptionHandling This operator performs the inner operators and delivers the result of the inner operators.
class	FeatureIterator This operator takes an input data set and applies its inner operators as often as the number of features of the input data is.
class	FeatureSubsetIteration This meta operator iterates through all possible feature subsets within the specified range and applies the inner operators on the feature subsets.
class	FileIterator This operator iterates over the files in the specified directory (and subdirectories if the corresponding parameter is set to true).
class	GridSearchParameterOptimizationOperator This operator finds the optimal values for a set of parameters using a grid search.
class	IteratingOperatorChain Performs its inner operators for the defined number of times.
class	IterativeWeightOptimization Performs an iterative feature selection guided by the AttributeWeights.
class	LearningCurveOperator This operator first divides the input example set into two parts, a training set and a test set according to the parameter "training_ratio".
class	MultipleLabelIterator Performs the inner operator for all label attributes, i.e. special attributes whose name starts with "label".
class	OperatorEnabler This operator can be used to enable and disable other operators.
class	OperatorSelector This operator can be used to employ a single inner operator or operator chain.
class	ParameterIteratingOperatorChain Provides an operator chain which operates on given parameters depending on specified values for these parameters.
class	ParameterIteration In contrast to the GridSearchParameterOptimizationOperator operator this operators simply uses the defined parameters and perform the inner operators for all possible combinations.
class	ParameterOptimizationOperator This operator provides basic functions for all other parameter optimization operators.
class	PartialExampleSetLearner This operator works similar to the LearningCurveOperator.
class	QuadraticParameterOptimizationOperator This operator finds the optimal values for a set of parameters using a quadratic interaction model.
class	RandomOptimizationChain This operator iterates several times through the inner operators and in each cycle evaluates a performance measure.
class	RatioSplitChain An operator chain that split an ExampleSet into two disjunct parts and applies the first child operator on the first part and applies the second child on the second part and the result of the first child.
class	RepeatUntilOperatorChain Performs its inner operators until all given criteria are met or a timeout occurs.
class	UnivariateLabelSeriesPrediction This operator can be used for some basic series prediction operations.
class	ValueIteration In each iteration step, this meta operator applies its inner operators to the input example set.
class	ValueSubgroupIteration In each iteration step, this meta operator applies its inner operators to a subset of the input example set.
class	WeightOptimization Performs a feature selection guided by the AttributeWeights.
class	XVPrediction Operator chain that splits an ExampleSet into a training and test sets similar to XValidation, but returns the test set predictions instead of a performance vector.

Uses of OperatorChain in com.rapidminer.operator.meta.branch

Subclasses of OperatorChain in com.rapidminer.operator.meta.branch

class

ProcessBranch This operator provides a conditional execution of parts of processes.

Uses of OperatorChain in com.rapidminer.operator.preprocessing

Subclasses of OperatorChain in com.rapidminer.operator.preprocessing

class

AttributeSubsetPreprocessing This operator can be used to select one attribute (or a subset) by defining a regular expression for the attribute name and applies its inner operators to the resulting subset.

Uses of OperatorChain in com.rapidminer.operator.preprocessing.filter

Subclasses of OperatorChain in com.rapidminer.operator.preprocessing.filter

class

MissingValueImputation The operator MissingValueImpution imputes missing values by learning models for each attribute (except the label) and applying those models to the data set.

Uses of OperatorChain in com.rapidminer.operator.preprocessing.series.filter

Subclasses of OperatorChain in com.rapidminer.operator.preprocessing.series.filter

class

Trend Adds a trend line for a specified series attributes by regressing a dummy variable onto the series attribute.

Uses of OperatorChain in com.rapidminer.operator.text

Subclasses of OperatorChain in com.rapidminer.operator.text

class

TextSegmenter This operator segments a text based on a starting and ending regular expression.

Uses of OperatorChain in com.rapidminer.operator.validation

Subclasses of OperatorChain in com.rapidminer.operator.validation

class	AbstractBootstrappingValidation This validation operator performs several bootstrapped samplings (sampling with replacement) on the input set and trains a model on these samples.
class	BatchSlidingWindowValidation The BatchSlidingWindowValidation is similar to the usual SlidingWindowValidation.
class	BatchXValidation BatchXValidation encapsulates a cross-validation process.
class	BootstrappingValidation This validation operator performs several bootstrapped samplings (sampling with replacement) on the input set and trains a model on these samples.
class	FixedSplitValidationChain A FixedSplitValidationChain splits up the example set at a fixed point into a training and test set and evaluates the model (linear sampling).
class	IteratingPerformanceAverage This operator chain performs the inner operators the given number of times.
class	RandomSplitValidationChain A RandomSplitValidationChain splits up the example set into a training and test set and evaluates the model.
class	RandomSplitWrapperValidationChain This operator evaluates the performance of feature weighting algorithms including feature selection.
class	SlidingWindowValidation This is a special validation chain which can only be used for series predictions where the time points are encoded as examples.
class	ValidationChain Abstract superclass of operator chains that split an ExampleSet into a training and test set and return a performance vector.
class	WeightedBootstrappingValidation This validation operator performs several bootstrapped samplings (sampling with replacement) on the input set and trains a model on these samples.
class	WrapperValidationChain This operator evaluates the performance of feature weighting algorithms including feature selection.
class	WrapperXValidation This operator evaluates the performance of feature weighting and selection algorithms.
class	XValidation XValidation encapsulates a cross-validation process.

Uses of OperatorChain in com.rapidminer.operator.visualization

Subclasses of OperatorChain in com.rapidminer.operator.visualization

class

ROCBasedComparisonOperator This operator uses its inner operators (each of those must produce a model) and calculates the ROC curve for each of them.