weka.classifiers
Class Bagging

java.lang.Object
  |
  +--weka.classifiers.Classifier
        |
        +--weka.classifiers.DistributionClassifier
              |
              +--weka.classifiers.Bagging

All Implemented Interfaces:

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

public class Bagging

extends DistributionClassifier

implements OptionHandler

Class for bagging a classifier. For more information, see

Leo Breiman (1996). Bagging predictors. Machine Learning, 24(2):123-140.

Valid options are:

-W classname
Specify the full class name of a weak classifier as the basis for bagging (required).

-I num
Set the number of bagging iterations (default 10).

-S seed
Random number seed for resampling (default 1).

-P num
Size of each bag, as a percentage of the training size (default 100).

Options after -- are passed to the designated classifier.

Author:

Eibe Frank (eibe@cs.waikato.ac.nz), Len Trigg (len@intelligenesis.net)

See Also:

Serialized Form

Field Summary

protected int	m_BagSizePercent The size of each bag sample, as a percentage of the training size
protected Classifier	m_Classifier The model base classifier to use
protected Classifier[]	m_Classifiers Array for storing the generated base classifiers.
protected int	m_NumIterations The number of iterations.
protected int	m_Seed The seed for random number generation.

Constructor Summary

Bagging()

Method Summary

void	buildClassifier(Instances data) Bagging method.
double[]	distributionForInstance(Instance instance) Calculates the class membership probabilities for the given test instance.
int	getBagSizePercent() Gets the size of each bag, as a percentage of the training set size.
Classifier	getClassifier() Get the classifier used as the classifier
int	getNumIterations() Gets the number of bagging iterations
java.lang.String[]	getOptions() Gets the current settings of the Classifier.
int	getSeed() Gets the seed for the random number generations
java.util.Enumeration	listOptions() Returns an enumeration describing the available options
static void	main(java.lang.String[] argv) Main method for testing this class.
void	setBagSizePercent(int newBagSizePercent) Sets the size of each bag, as a percentage of the training set size.
void	setClassifier(Classifier newClassifier) Set the classifier for bagging.
void	setNumIterations(int numIterations) Sets the number of bagging iterations
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setSeed(int seed) Set the seed for random number generation.
java.lang.String	toString() Returns description of the bagged classifier.

Methods inherited from class weka.classifiers.DistributionClassifier

classifyInstance

Methods inherited from class weka.classifiers.Classifier

forName, makeCopies

Methods inherited from class java.lang.Object

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

Field Detail

m_Classifier

protected Classifier m_Classifier

The model base classifier to use

m_Classifiers

protected Classifier[] m_Classifiers

Array for storing the generated base classifiers.

m_NumIterations

protected int m_NumIterations

The number of iterations.

m_Seed

protected int m_Seed

The seed for random number generation.

m_BagSizePercent

protected int m_BagSizePercent

The size of each bag sample, as a percentage of the training size

Constructor Detail

Bagging

public Bagging()

Method Detail

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options

Specified by:

listOptions in interface OptionHandler

Returns:

an enumeration of all the available options

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception

Parses a given list of options. Valid options are:

-W classname
Specify the full class name of a weak classifier as the basis for bagging (required).

-I num
Set the number of bagging iterations (default 10).

-S seed
Random number seed for resampling (default 1).

-P num
Size of each bag, as a percentage of the training size (default 100).

Options after -- are passed to the designated classifier.

Specified by:

setOptions in interface OptionHandler

Parameters:

options - the list of options as an array of strings

Throws:

java.lang.Exception - if an option is not supported

getOptions

public java.lang.String[] getOptions()

Gets the current settings of the Classifier.

Specified by:

getOptions in interface OptionHandler

Returns:

an array of strings suitable for passing to setOptions

setClassifier

public void setClassifier(Classifier newClassifier)

Set the classifier for bagging.

Parameters:

newClassifier - the Classifier to use.

getClassifier

public Classifier getClassifier()

Get the classifier used as the classifier

Returns:

the classifier used as the classifier

getBagSizePercent

public int getBagSizePercent()

Gets the size of each bag, as a percentage of the training set size.

Returns:

the bag size, as a percentage.

setBagSizePercent

public void setBagSizePercent(int newBagSizePercent)

Sets the size of each bag, as a percentage of the training set size.

Parameters:

newBagSizePercent - the bag size, as a percentage.

setNumIterations

public void setNumIterations(int numIterations)

Sets the number of bagging iterations

getNumIterations

public int getNumIterations()

Gets the number of bagging iterations

Returns:

the maximum number of bagging iterations

setSeed

public void setSeed(int seed)

Set the seed for random number generation.

Parameters:

seed - the seed

getSeed

public int getSeed()

Gets the seed for the random number generations

Returns:

the seed for the random number generation

buildClassifier

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

Bagging method.

Overrides:

buildClassifier in class Classifier

Parameters:

data - the training data to be used for generating the bagged classifier.

Throws:

java.lang.Exception - if the classifier could not be built successfully

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception

Calculates the class membership probabilities for the given test instance.

Overrides:

distributionForInstance in class DistributionClassifier

Parameters:

instance - the instance to be classified

Returns:

preedicted class probability distribution

Throws:

java.lang.Exception - if distribution can't be computed successfully

toString

public java.lang.String toString()

Returns description of the bagged classifier.

Overrides:

toString in class java.lang.Object

Returns:

description of the bagged classifier as a string

main

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

Main method for testing this class.

Parameters:

argv - the options