Uses of Class
com.rapidminer.operator.features.weighting.AbstractWeighting

Packages that use AbstractWeighting

com.rapidminer.operator.features.weighting

Operators to weight features or determine feature relevance.

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

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

class	AbstractEntropyWeighting This operator calculates the relevance of a feature by computing the an entropy value of the class distribution, if the given example set would have been splitted according to the feature.
class	ChiSquaredWeighting 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.
class	CorpusBasedFeatureWeighting This operator uses a corpus of examples to characterize a single class by setting feature weights.
class	CorrelationWeighting This class provides a weighting scheme based upon correlation.
class	GenericWekaAttributeWeighting Performs the AttributeEvaluator of Weka with the same name to determine a sort of attribute relevance.
class	GiniWeighting This operator calculates the relevance of a feature by computing the Gini index of the class distribution, if the given example set would have been splitted according to the feature.
class	InfoGainRatioWeighting This operator calculates the relevance of a feature by computing the information gain ratio for the class distribution (if exampleSet would have been splitted according to each of the given features).
class	InfoGainWeighting This operator calculates the relevance of a feature by computing the information gain in class distribution, if exampleSet would be splitted after the feature.
class	NameBasedWeighting This operator is able to create feature weights based on regular expressions defined for the feature names.
class	OneRErrorWeighting This operator calculates the relevance of a feature by computing the error rate of a OneR Model on the exampleSet without this feature.
class	PCAWeighting Uses the factors of one of the principal components (default is the first) as feature weights.
class	ReliefWeighting 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.
class	StandardDeviationWeighting Creates weights from the standard deviations of all attributes.
class	SVMWeighting Uses the coefficients of the normal vector of a linear SVM as feature weights.
class	SymmetricalUncertaintyOperator This operator calculates the relevance of an attribute by measuring the symmetrical uncertainty with respect to the class.