weka.classifiers.trees.j48

Class InfoGainSplitCrit

java.lang.Object

weka.classifiers.trees.j48.SplitCriterion

weka.classifiers.trees.j48.EntropyBasedSplitCrit

weka.classifiers.trees.j48.InfoGainSplitCrit

All Implemented Interfaces:

java.io.Serializable, RevisionHandler

public final class InfoGainSplitCrit
extends EntropyBasedSplitCrit

Class for computing the information gain for a given distribution.

Version:

$Revision: 1.10 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz)

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
InfoGainSplitCrit()

Method Summary

Modifier and Type	Method and Description
java.lang.String	getRevision() Returns the revision string.
double	splitCritValue(Distribution bags) This method is a straightforward implementation of the information gain criterion for the given distribution.
double	splitCritValue(Distribution bags, double totalNoInst) This method computes the information gain in the same way C4.5 does.
double	splitCritValue(Distribution bags, double totalNoInst, double oldEnt) This method computes the information gain in the same way C4.5 does.

Methods inherited from class weka.classifiers.trees.j48.EntropyBasedSplitCrit

logFunc, newEnt, oldEnt, splitEnt

Methods inherited from class weka.classifiers.trees.j48.SplitCriterion

splitCritValue, splitCritValue, splitCritValue

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

InfoGainSplitCrit

public InfoGainSplitCrit()

Method Detail

splitCritValue

public final double splitCritValue(Distribution bags)

This method is a straightforward implementation of the information gain criterion for the given distribution.

Overrides:

splitCritValue in class SplitCriterion

Returns:

value of splitting criterion. 0 by default

splitCritValue

public final double splitCritValue(Distribution bags,
                                   double totalNoInst)

This method computes the information gain in the same way C4.5 does.

Parameters:

bags - the distribution

totalNoInst - weight of ALL instances (including the ones with missing values).

splitCritValue

public final double splitCritValue(Distribution bags,
                                   double totalNoInst,
                                   double oldEnt)

This method computes the information gain in the same way C4.5 does.

Parameters:

bags - the distribution

totalNoInst - weight of ALL instances

oldEnt - entropy with respect to "no-split"-model.

getRevision

public java.lang.String getRevision()

Returns the revision string.

Returns:

the revision