Example usage for weka.attributeSelection ASEvaluation subclass-usage

Introduction

In this page you can find the example usage for weka.attributeSelection ASEvaluation subclass-usage.

Usage

From source file GainRatioAttributeEval1.java

/** 
 <!-- globalinfo-start -->
 * GainRatioAttributeEval :<br/>
 * <br/>
 * Evaluates the worth of an attribute by measuring the gain ratio with respect to the class.<br/>
 * <br/>

From source file WrapperSubset.java

/**
 * <!-- globalinfo-start --> WrapperSubsetEval:<br/>
 * <br/>
 * Evaluates attribute sets by using a learning scheme. Cross validation is used
 * to estimate the accuracy of the learning scheme for a set of attributes.<br/>
 * <br/>

From source file br.com.ufu.lsi.utils.DocumentFrequencyAttributeEval.java

public class DocumentFrequencyAttributeEval extends ASEvaluation implements AttributeEvaluator, OptionHandler {

    /** for serialization */
    static final long serialVersionUID = -1949849576589218930L;

    /** Treat missing values as a seperate value */

From source file ChiSquare.ChiSquaredAttributeEval.java

/** 
 <!-- globalinfo-start -->
 * ChiSquaredAttributeEval :<br/>
 * <br/>
 * Evaluates the worth of an attribute by computing the value of the chi-squared statistic with respect to the class.<br/>
 * <p/>

From source file edu.columbia.cs.ltrie.sampling.queries.generation.ChiSquaredWithYatesCorrectionAttributeEval.java

/** 
 <!-- globalinfo-start -->
 * ChiSquaredWithYatesCorrectionAttributeEval :<br/>
 * <br/>
 * Evaluates the worth of an attribute by computing the value of the chi-squared statistic with respect to the class.<br/>
 * <p/>

From source file feature.InfoGainEval.java

/**
 * <!-- globalinfo-start --> InfoGainAttributeEval :<br/>
 * <br/>
 * Evaluates the worth of an attribute by measuring the information gain with
 * respect to the class.<br/>
 * <br/>

From source file FeatureSelection.ReliefFAttributeEval.java

/**
 * <!-- globalinfo-start --> ReliefFAttributeEval :<br/>
 * <br/>
 * Evaluates the worth of an attribute by repeatedly sampling an instance and
 * considering the value of the given attribute for the nearest instance of the
 * same and different class. Can operate on both discrete and continuous class

From source file ml.dataprocess.CorrelationAttributeEval.java

/**
 <!-- globalinfo-start -->
 * CorrelationAttributeEval :<br/>
 * <br/>
 * Evaluates the worth of an attribute by measuring the correlation (Pearson's) between it and the class.<br/>
 * <br/>

From source file moa.reduction.bayes.IncrInfoThAttributeEval.java

/**
 * <!-- globalinfo-start --> InfoGainAttributeEval :<br/>
 * <br/>
 * Evaluates the worth of an attribute by measuring the information gain with
 * respect to the class.<br/>
 * <br/>

From source file moa.reduction.bayes.OFSGDAttributeEval.java

/**
 * <!-- globalinfo-start --> OFSGDAttributeEval :<br/>
 * <br/>
 * Evaluates the worth of an attribute through the computation of weights 
 * using a linear classifier with sparse projection.<br/>
 * <br/>

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