weka.classifiers.trees
Class Id3

java.lang.Object
  weka.classifiers.Classifier
      weka.classifiers.trees.Id3

All Implemented Interfaces:

java.lang.Cloneable, OptionHandler, java.io.Serializable

public class Id3

extends Classifier

Class implementing an Id3 decision tree classifier. For more information, see

R. Quinlan (1986). Induction of decision trees. Machine Learning. Vol.1, No.1, pp. 81-106.

Version:

$Revision: 1.11 $

Author:

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

See Also:

Serialized Form

Field Summary

private Attribute	m_Attribute Attribute used for splitting.
private Attribute	m_ClassAttribute Class attribute of dataset.
private double	m_ClassValue Class value if node is leaf.
private double[]	m_Distribution Class distribution if node is leaf.
private Id3[]	m_Successors The node's successors.

Fields inherited from class weka.classifiers.Classifier

m_Debug

Constructor Summary

Id3()

Method Summary

void	buildClassifier(Instances data) Builds Id3 decision tree classifier.
double	classifyInstance(Instance instance) Classifies a given test instance using the decision tree.
private double	computeEntropy(Instances data) Computes the entropy of a dataset.
private double	computeInfoGain(Instances data, Attribute att) Computes information gain for an attribute.
double[]	distributionForInstance(Instance instance) Computes class distribution for instance using decision tree.
java.lang.String	globalInfo() Returns a string describing classifier
static void	main(java.lang.String[] args) Main method.
private void	makeTree(Instances data) Method building Id3 tree.
private Instances[]	splitData(Instances data, Attribute att) Splits a dataset according to the values of a nominal attribute.
java.lang.String	toString() Prints the decision tree using the private toString method from below.
private java.lang.String	toString(int level) Outputs a tree at a certain level.

Methods inherited from class weka.classifiers.Classifier

debugTipText, forName, getDebug, getOptions, listOptions, makeCopies, setDebug, setOptions

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

m_Successors

private Id3[] m_Successors

The node's successors.

m_Attribute

private Attribute m_Attribute

Attribute used for splitting.

m_ClassValue

private double m_ClassValue

Class value if node is leaf.

m_Distribution

private double[] m_Distribution

Class distribution if node is leaf.

m_ClassAttribute

private Attribute m_ClassAttribute

Class attribute of dataset.

Constructor Detail

Id3

public Id3()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing classifier

Returns:

a description suitable for displaying in the explorer/experimenter gui

buildClassifier

public void buildClassifier(Instances data)
                     throws java.lang.Exception

Builds Id3 decision tree classifier.

Specified by:

buildClassifier in class Classifier

Parameters:

data - the training data

Throws:

java.lang.Exception - if classifier can't be built successfully

makeTree

private void makeTree(Instances data)
               throws java.lang.Exception

Method building Id3 tree.

Parameters:

data - the training data

Throws:

java.lang.Exception - if decision tree can't be built successfully

classifyInstance

public double classifyInstance(Instance instance)

Classifies a given test instance using the decision tree.

Overrides:

classifyInstance in class Classifier

Parameters:

instance - the instance to be classified

Returns:

the classification

distributionForInstance

public double[] distributionForInstance(Instance instance)

Computes class distribution for instance using decision tree.

Overrides:

distributionForInstance in class Classifier

Parameters:

instance - the instance for which distribution is to be computed

Returns:

the class distribution for the given instance

toString

public java.lang.String toString()

Prints the decision tree using the private toString method from below.

Returns:

a textual description of the classifier

computeInfoGain

private double computeInfoGain(Instances data,
                               Attribute att)
                        throws java.lang.Exception

Computes information gain for an attribute.

Parameters:

data - the data for which info gain is to be computed

att - the attribute

Returns:

the information gain for the given attribute and data

Throws:

java.lang.Exception

computeEntropy

private double computeEntropy(Instances data)
                       throws java.lang.Exception

Computes the entropy of a dataset.

Parameters:

data - the data for which entropy is to be computed

Returns:

the entropy of the data's class distribution

Throws:

java.lang.Exception

splitData

private Instances[] splitData(Instances data,
                              Attribute att)

Splits a dataset according to the values of a nominal attribute.

Parameters:

data - the data which is to be split

att - the attribute to be used for splitting

Returns:

the sets of instances produced by the split

toString

private java.lang.String toString(int level)

Outputs a tree at a certain level.

Parameters:

level - the level at which the tree is to be printed

main

public static void main(java.lang.String[] args)

Main method.

Parameters:

args - the options for the classifier