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java.lang.Objectcom.rapidminer.operator.Operator
com.rapidminer.operator.performance.AbstractExampleSetEvaluator
com.rapidminer.operator.validation.CFSFeatureSetEvaluator
public class CFSFeatureSetEvaluator
CFS attribute subset evaluator. For more information see:
Hall, M. A.
(1998). Correlation-based Feature Subset Selection for Machine Learning.
Thesis submitted in partial fulfilment of the requirements of the degree of
Doctor of Philosophy at the University of Waikato.
This operator creates a filter based performance measure for a feature subset. It evaluates the worth of a subset of attributes by considering the individual predictive ability of each feature along with the degree of redundancy between them. Subsets of features that are highly correlated with the class while having low intercorrelation are preferred.
This operator can be applied on both numerical and nominal data sets.
| Constructor Summary | |
|---|---|
CFSFeatureSetEvaluator(OperatorDescription description)
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| Method Summary | |
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PerformanceVector |
evaluate(ExampleSet exampleSet)
Implements the evaluation. |
InputDescription |
getInputDescription(java.lang.Class cls)
Shows a parameter keep_example_set with default value "false". |
| Methods inherited from class com.rapidminer.operator.performance.AbstractExampleSetEvaluator |
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apply, getInputClasses, getOutputClasses |
| Methods inherited from class java.lang.Object |
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clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait |
| Constructor Detail |
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public CFSFeatureSetEvaluator(OperatorDescription description)
| Method Detail |
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public InputDescription getInputDescription(java.lang.Class cls)
getInputDescription in class Operator
public PerformanceVector evaluate(ExampleSet exampleSet)
throws OperatorException
AbstractExampleSetEvaluatorAbstractExampleSetEvaluator.apply().
evaluate in class AbstractExampleSetEvaluatorOperatorException
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