weka.classifiers.meta

Class RUSBoost

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.SingleClassifierEnhancer

weka.classifiers.IteratedSingleClassifierEnhancer

weka.classifiers.RandomizableIteratedSingleClassifierEnhancer

weka.classifiers.meta.RUSBoost

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, weka.classifiers.Classifier, weka.classifiers.Sourcable, weka.core.CapabilitiesHandler, weka.core.OptionHandler, weka.core.Randomizable, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler, weka.core.WeightedInstancesHandler

public class RUSBoost
extends weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
implements weka.core.WeightedInstancesHandler, weka.classifiers.Sourcable, weka.core.TechnicalInformationHandler

Class for boosting a binary class classifier using the RUSboost method.
BibTeX:

 @article{C. Seiffert and  T. Khoshgoftaar and  J. Van Hulse and  A. Napolitano},
 title = {Rusboost: A hybrid approach to alleviating class imbalance},
 journal = {IEEE Transactions on Systems and  Man and Cybernetics and  Part A},
 volume = {40},
 number = {1},
 pages = {185--197},
 year = {2010}
 }
 

Valid options are:

 -rusP <Percentage>
  Specifies the proportion of final majority class respecting the minority class. (default 75).
  If the percentage specified is higher than the current majority percentage, the filter does nothing,
  else, the filter eliminates majority classes randomly until the majority proportion reaches the specified percentage.
 

 -P <num>
  Percentage of weight mass to base training on.
  (default 100, reduce to around 90 speed up)
 

 -Q
  Use resampling for boosting.
 

 -S <num>
  Random number seed.
  (default 1)
 

 -I <num>
  Number of iterations.
  (default 10)
 

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
 

 -W
  Full name of base classifier.
  (default: weka.classifiers.trees.DecisionStump)
 

 Options specific to classifier weka.classifiers.trees.DecisionStump:
 

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
 

Options after -- are passed to the designated classifier.

Version:

$Revision: 1 $

Author:

Sergio García Charameli, Daniel Rodríguez - University of Alcalá

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
RUSBoost() Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
void	buildClassifier(weka.core.Instances data) Boosting method.
double[]	distributionForInstance(weka.core.Instance instance) Calculates the class membership probabilities for the given test instance.
weka.core.Capabilities	getCapabilities() Returns default capabilities of the classifier.
java.lang.String[]	getOptions() Gets the current settings of the Classifier.
java.lang.String	getRevision() Returns the revision string.
double	getRUS_Percentage() Gets the percentage of the proportion of majority class.
weka.core.TechnicalInformation	getTechnicalInformation() Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
boolean	getUseResampling() Get whether resampling is turned on
int	getWeightThreshold() Get the degree of weight thresholding
java.lang.String	globalInfo() Returns a string describing classifier
weka.filters.supervised.instance.RUS	initRUS()
java.util.Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(java.lang.String[] argv) Main method for testing this class.
java.lang.String	RUS_percentageTipText() Returns the tip text for this property.
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setRUS_Percentage(double value) Sets the percentage of the proportion of majority class.
void	setUseResampling(boolean r) Set resampling mode
void	setWeightThreshold(int threshold) Set weight threshold
java.lang.String	toSource(java.lang.String className) Returns the boosted model as Java source code.
java.lang.String	toString() Returns description of the boosted classifier.
java.lang.String	useResamplingTipText() Returns the tip text for this property
java.lang.String	weightThresholdTipText() Returns the tip text for this property

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.AbstractClassifier

classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, runClassifier, setDebug

Methods inherited from class java.lang.Object

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

Constructor Detail

RUSBoost

public RUSBoost()

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

getTechnicalInformation

public weka.core.TechnicalInformation getTechnicalInformation()

Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

Specified by:

getTechnicalInformation in interface weka.core.TechnicalInformationHandler

Returns:

the technical information about this class

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface weka.core.OptionHandler

Overrides:

listOptions in class weka.classifiers.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:

 -rusP <Percentage>
  Specifies the proportion of final majority class respecting the minority class. (default 75).
  If the percentage specified is higher than the current majority percentage, the filter does nothing,
  else, the filter eliminates majority classes randomly until the majority proportion reaches the specified percentage.
 

 -P <num>
  Percentage of weight mass to base training on.
  (default 100, reduce to around 90 speed up)
 

 -Q
  Use resampling for boosting.
 

 -S <num>
  Random number seed.
  (default 1)
 

 -I <num>
  Number of iterations.
  (default 10)
 

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
 

 -W
  Full name of base classifier.
  (default: weka.classifiers.trees.DecisionStump)
 

 Options specific to classifier weka.classifiers.trees.DecisionStump:
 

 -D
  If set, classifier is run in debug mode and
  may output additional info to the console
 

Options after -- are passed to the designated classifier.

Specified by:

setOptions in interface weka.core.OptionHandler

Overrides:

setOptions in class weka.classifiers.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 weka.core.OptionHandler

Overrides:

getOptions in class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer

Returns:

an array of strings suitable for passing to setOptions

weightThresholdTipText

public java.lang.String weightThresholdTipText()

Returns the tip text for this property

Returns:

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

setWeightThreshold

public void setWeightThreshold(int threshold)

Set weight threshold

Parameters:

threshold - the percentage of weight mass used for training

getWeightThreshold

public int getWeightThreshold()

Get the degree of weight thresholding

Returns:

the percentage of weight mass used for training

useResamplingTipText

public java.lang.String useResamplingTipText()

Returns the tip text for this property

Returns:

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

setUseResampling

public void setUseResampling(boolean r)

Set resampling mode

Parameters:

r - true if resampling should be done

getUseResampling

public boolean getUseResampling()

Get whether resampling is turned on

Returns:

true if resampling output is on

RUS_percentageTipText

public java.lang.String RUS_percentageTipText()

Returns the tip text for this property.

Returns:

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

setRUS_Percentage

public void setRUS_Percentage(double value)

Sets the percentage of the proportion of majority class.

Parameters:

value - the percentage to use

getRUS_Percentage

public double getRUS_Percentage()

Gets the percentage of the proportion of majority class.

Returns:

the percentage of the proportion of majority class.

initRUS

public weka.filters.supervised.instance.RUS initRUS()

getCapabilities

public weka.core.Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface weka.classifiers.Classifier

Specified by:

getCapabilities in interface weka.core.CapabilitiesHandler

Overrides:

getCapabilities in class weka.classifiers.SingleClassifierEnhancer

Returns:

the capabilities of this classifier

See Also:

Capabilities

buildClassifier

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

Boosting method.

Specified by:

buildClassifier in interface weka.classifiers.Classifier

Overrides:

buildClassifier in class weka.classifiers.IteratedSingleClassifierEnhancer

Parameters:

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

Throws:

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

distributionForInstance

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

Calculates the class membership probabilities for the given test instance.

Specified by:

distributionForInstance in interface weka.classifiers.Classifier

Overrides:

distributionForInstance in class weka.classifiers.AbstractClassifier

Parameters:

instance - the instance to be classified

Returns:

predicted class probability distribution

Throws:

java.lang.Exception - if instance could not be classified successfully

toSource

public java.lang.String toSource(java.lang.String className)
                          throws java.lang.Exception

Returns the boosted model as Java source code.

Specified by:

toSource in interface weka.classifiers.Sourcable

Parameters:

className - the classname of the generated class

Returns:

the tree as Java source code

Throws:

java.lang.Exception - if something goes wrong

toString

public java.lang.String toString()

Returns description of the boosted classifier.

Overrides:

toString in class java.lang.Object

Returns:

description of the boosted classifier as a string

getRevision

public java.lang.String getRevision()

Returns the revision string.

Specified by:

getRevision in interface weka.core.RevisionHandler

Overrides:

getRevision in class weka.classifiers.AbstractClassifier

Returns:

the revision

main

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

Main method for testing this class.

Parameters:

argv - the options