InfoGainSplitCrit (DiscriminationAwareProcessAnalysis)

java.lang.Object
- org.processmining.plugins.workshop.Yaguang.WekaDiscriminationTree.SplitCriterion
- - org.processmining.plugins.workshop.Yaguang.WekaDiscriminationTree.EntropyBasedSplitCrit
  - - org.processmining.plugins.workshop.Yaguang.WekaDiscriminationTree.InfoGainSplitCrit

All Implemented Interfaces:

java.io.Serializable
```
public final class InfoGainSplitCrit
extends EntropyBasedSplitCrit
```
Class for computing the information gain for a given distribution.

Version:

$Revision: 1.6 $

Author:

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

See Also:

Serialized Form

Field Summary
- Fields inherited from class org.processmining.plugins.workshop.Yaguang.WekaDiscriminationTree.EntropyBasedSplitCrit
  log2

Constructor Summary

Constructors
Constructor Description

InfoGainSplitCrit()

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`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 org.processmining.plugins.workshop.Yaguang.WekaDiscriminationTree.EntropyBasedSplitCrit
logFunc, newEnt, oldEnt, splitEnt

Methods inherited from class org.processmining.plugins.workshop.Yaguang.WekaDiscriminationTree.SplitCriterion
splitCritValue, splitCritValue, splitCritValue

Methods inherited from class java.lang.Object
clone, equals, finalize, 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:
    
    distribution - 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:
    
    distribution - the distribution
    
    totalNoInst - weight of ALL instances
    
    oldEnt - entropy with respect to "no-split"-model.