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Class weka.classifiers.AdaBoostM1

java.lang.Object
   |
   +----weka.classifiers.Classifier
           |
           +----weka.classifiers.DistributionClassifier
                   |
                   +----weka.classifiers.AdaBoostM1

public class AdaBoostM1

extends DistributionClassifier

implements OptionHandler, WeightedInstancesHandler, Sourcable

Yoav Freund and Robert E. Schapire (1996). Experiments with a new boosting algorithm. Proc International Conference on Machine Learning, pages 148-156, Morgan Kaufmann, San Francisco.

Valid options are:

-D
Turn on debugging output.

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

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

-P num
Set the percentage of weight mass used to build classifiers (default 100).

-Q
Use resampling instead of reweighting.

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

Options after -- are passed to the designated classifier.

Version:

$Revision: 1.10 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz), Len Trigg (trigg@cs.waikato.ac.nz)

AdaBoostM1()

buildClassifier(Instances)

Boosting method.

distributionForInstance(Instance)

Calculates the class membership probabilities for the given test instance.

getClassifier()

Get the classifier used as the classifier

getDebug()

Get whether debugging is turned on

getMaxIterations()

Get the maximum number of boost iterations

getOptions()

Gets the current settings of the Classifier.

getSeed()

Get seed for resampling.

getUseResampling()

Get whether resampling is turned on

getWeightThreshold()

Get the degree of weight thresholding

listOptions()

Returns an enumeration describing the available options

main(String[])

Main method for testing this class.

setClassifier(Classifier)

Set the classifier for boosting.

setDebug(boolean)

Set debugging mode

setMaxIterations(int)

Set the maximum number of boost iterations

setOptions(String[])

Parses a given list of options.

setSeed(int)

Set seed for resampling.

setUseResampling(boolean)

Set resampling mode

setWeightThreshold(int)

Set weight threshold

toSource(String)

Returns the boosted model as Java source code.

toString()

Returns description of the boosted classifier.

 public AdaBoostM1()

 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:

-D
Turn on debugging output.

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

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

-P num
Set the percentage of weight mass used to build classifiers (default 100).

-Q
Use resampling instead of reweighting.

-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 setMaxIterations(int maxIterations)

Set the maximum number of boost iterations

 public int getMaxIterations()

Get the maximum number of boost iterations

Returns:

the maximum number of boost iterations

 public void setWeightThreshold(int threshold)

Set weight threshold

Parameters:

thresholding - the percentage of weight mass used for training

 public int getWeightThreshold()

Get the degree of weight thresholding

Returns:

the percentage of weight mass used for training

 public void setSeed(int seed)

Set seed for resampling.

Parameters:

seed - the seed for resampling

 public int getSeed()

Get seed for resampling.

Returns:

the seed for resampling

 public void setDebug(boolean debug)

Set debugging mode

Parameters:

debug - true if debug output should be printed

 public boolean getDebug()

Get whether debugging is turned on

Returns:

true if debugging output is on

 public void setUseResampling(boolean r)

Set resampling mode

Parameters:

resampling - true if resampling should be done

 public boolean getUseResampling()

Get whether resampling is turned on

Returns:

true if resampling output is on

 public void buildClassifier(Instances data) throws Exception

Boosting method.

Parameters:

data - the training data to be used for generating the boosted 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:

predicted class probability distribution

Throws: Exception

if instance could not be classified successfully

Overrides:

distributionForInstance in class DistributionClassifier

 public String toSource(String className) throws Exception

Returns the boosted model as Java source code.

Returns:

the tree as Java source code

Throws: Exception

if something goes wrong

 public String toString()

Returns description of the boosted classifier.

Returns:

description of the boosted 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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