weka.classifiers.meta
Class Bagging

java.lang.Object
  |
  +--weka.classifiers.Classifier
        |
        +--weka.classifiers.SingleClassifierEnhancer
              |
              +--weka.classifiers.IteratedSingleClassifierEnhancer
                    |
                    +--weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
                          |
                          +--weka.classifiers.meta.Bagging

All Implemented Interfaces:

AdditionalMeasureProducer, java.lang.Cloneable, OptionHandler, Randomizable, java.io.Serializable, WeightedInstancesHandler

public class Bagging

extends RandomizableIteratedSingleClassifierEnhancer

implements WeightedInstancesHandler, AdditionalMeasureProducer

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).

-O
Compute out of bag error.

Options after -- are passed to the designated classifier.

Version:

$Revision: 1.29 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz), Len Trigg (len@reeltwo.com), Richard Kirkby (rkirkby@cs.waikato.ac.nz)

See Also:

Serialized Form

Constructor Summary

Bagging()
Constructor.

Method Summary

java.lang.String	bagSizePercentTipText() Returns the tip text for this property
void	buildClassifier(Instances data) Bagging method.
java.lang.String	calcOutOfBagTipText() Returns the tip text for this property
double[]	distributionForInstance(Instance instance) Calculates the class membership probabilities for the given test instance.
java.util.Enumeration	enumerateMeasures() Returns an enumeration of the additional measure names.
int	getBagSizePercent() Gets the size of each bag, as a percentage of the training set size.
boolean	getCalcOutOfBag() Get whether the out of bag error is calculated.
double	getMeasure(java.lang.String additionalMeasureName) Returns the value of the named measure.
java.lang.String[]	getOptions() Gets the current settings of the Classifier.
java.lang.String	globalInfo() Returns a string describing classifier
java.util.Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(java.lang.String[] argv) Main method for testing this class.
double	measureOutOfBagError() Gets the out of bag error that was calculated as the classifier was built.
Instances	resampleWithWeights(Instances data, java.util.Random random, boolean[] sampled) Creates a new dataset of the same size using random sampling with replacement according to the given weight vector.
void	setBagSizePercent(int newBagSizePercent) Sets the size of each bag, as a percentage of the training set size.
void	setCalcOutOfBag(boolean calcOutOfBag) Set whether the out of bag error is calculated.
void	setOptions(java.lang.String[] options) Parses a given list of options.
java.lang.String	toString() Returns description of the bagged classifier.

Methods inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer

getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer

getNumIterations, numIterationsTipText, setNumIterations

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

classifierTipText, getClassifier, setClassifier

Methods inherited from class weka.classifiers.Classifier

classifyInstance, debugTipText, forName, getDebug, makeCopies, setDebug

Methods inherited from class java.lang.Object

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

Constructor Detail

Bagging

public Bagging()

Constructor.

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing classifier

Returns:

a description suitable for displaying in the explorer/experimenter gui

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class RandomizableIteratedSingleClassifierEnhancer

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).

-O
Compute out of bag error.

Options after -- are passed to the designated classifier.

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class RandomizableIteratedSingleClassifierEnhancer

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

Overrides:

getOptions in class RandomizableIteratedSingleClassifierEnhancer

Returns:

an array of strings suitable for passing to setOptions

bagSizePercentTipText

public java.lang.String bagSizePercentTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

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.

calcOutOfBagTipText

public java.lang.String calcOutOfBagTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

setCalcOutOfBag

public void setCalcOutOfBag(boolean calcOutOfBag)

Set whether the out of bag error is calculated.

Parameters:

calcOutOfBag - whether to calculate the out of bag error

getCalcOutOfBag

public boolean getCalcOutOfBag()

Get whether the out of bag error is calculated.

Returns:

whether the out of bag error is calculated

measureOutOfBagError

public double measureOutOfBagError()

Gets the out of bag error that was calculated as the classifier was built.

Returns:

the out of bag error

enumerateMeasures

public java.util.Enumeration enumerateMeasures()

Returns an enumeration of the additional measure names.

Specified by:

enumerateMeasures in interface AdditionalMeasureProducer

Returns:

an enumeration of the measure names

getMeasure

public double getMeasure(java.lang.String additionalMeasureName)

Returns the value of the named measure.

Specified by:

getMeasure in interface AdditionalMeasureProducer

Parameters:

additionalMeasureName - the name of the measure to query for its value

Returns:

the value of the named measure

Throws:

java.lang.IllegalArgumentException - if the named measure is not supported

resampleWithWeights

public final Instances resampleWithWeights(Instances data,
                                           java.util.Random random,
                                           boolean[] sampled)

Creates a new dataset of the same size using random sampling with replacement according to the given weight vector. The weights of the instances in the new dataset are set to one. The length of the weight vector has to be the same as the number of instances in the dataset, and all weights have to be positive.

Parameters:

data - the data to be sampled from

random - a random number generator

sampled - indicating which instance has been sampled

Returns:

the new dataset

Throws:

java.lang.IllegalArgumentException - if the weights array is of the wrong length or contains negative weights.

buildClassifier

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

Bagging method.

Overrides:

buildClassifier in class IteratedSingleClassifierEnhancer

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 Classifier

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