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Class weka.classifiers.Bagging

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

public class Bagging

extends DistributionClassifier

implements OptionHandler

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 boosting (required).

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

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

Options after -- are passed to the designated classifier.

Version:

$Revision: 1.8 $

Author:

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

Bagging()

buildClassifier(Instances)

Bagging method.

distributionForInstance(Instance)

Calculates the class membership probabilities for the given test instance.

getClassifier()

Get the classifier used as the classifier

getNumIterations()

Gets the number of bagging iterations

getOptions()

Gets the current settings of the Classifier.

getSeed()

Gets the seed for the random number generations

listOptions()

Returns an enumeration describing the available options

main(String[])

Main method for testing this class.

setClassifier(Classifier)

Set the classifier for boosting.

setNumIterations(int)

Sets the number of bagging iterations

setOptions(String[])

Parses a given list of options.

setSeed(int)

Set the seed for random number generation.

toString()

Returns description of the bagged classifier.

 public Bagging()

 public Enumeration listOptions()

Returns an enumeration describing the available options

Returns:

an enumeration of all the available options

 public void setOptions(String options[]) throws 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 boosting (required).

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

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

Options after -- are passed to the designated classifier.

Parameters:

options - the list of options as an array of strings

Throws: Exception

if an option is not supported

 public String[] getOptions()

Gets the current settings of the Classifier.

Returns:

an array of strings suitable for passing to setOptions

 public void setClassifier(Classifier newClassifier)

Set the classifier for boosting.

Parameters:

newClassifier - the Classifier to use.

 public Classifier getClassifier()

Get the classifier used as the classifier

Returns:

the classifier used as the classifier

 public void setNumIterations(int numIterations)

Sets the number of bagging iterations

 public int getNumIterations()

Gets the number of bagging iterations

Returns:

the maximum number of bagging iterations

 public void setSeed(int seed)

Set the seed for random number generation.

Parameters:

seed - the seed

 public int getSeed()

Gets the seed for the random number generations

Returns:

the seed for the random number generation

 public void buildClassifier(Instances data) throws Exception

Bagging method.

Parameters:

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

Throws: Exception

if the classifier could not be built successfully

Overrides:

buildClassifier in class Classifier

 public double[] distributionForInstance(Instance instance) throws Exception

Calculates the class membership probabilities for the given test instance.

Parameters:

instance - the instance to be classified

Returns:

preedicted class probability distribution

Throws: Exception

if distribution can't be computed successfully

Overrides:

distributionForInstance in class DistributionClassifier

 public String toString()

Returns description of the bagged classifier.

Returns:

description of the bagged classifier as a string

Overrides:

toString in class Object

 public static void main(String argv[])

Main method for testing this class.

Parameters:

argv - the options

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