weka.classifiers.mi

Class MIBoost

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.SingleClassifierEnhancer

weka.classifiers.mi.MIBoost

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, weka.classifiers.Classifier, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.MultiInstanceCapabilitiesHandler, weka.core.OptionHandler, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler

public class MIBoost
extends weka.classifiers.SingleClassifierEnhancer
implements weka.core.OptionHandler, weka.core.MultiInstanceCapabilitiesHandler, weka.core.TechnicalInformationHandler

MI AdaBoost method, considers the geometric mean of posterior of instances inside a bag (arithmatic mean of log-posterior) and the expectation for a bag is taken inside the loss function.

For more information about Adaboost, see:

Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.

BibTeX:

 @inproceedings{Freund1996,
    address = {San Francisco},
    author = {Yoav Freund and Robert E. Schapire},
    booktitle = {Thirteenth International Conference on Machine Learning},
    pages = {148-156},
    publisher = {Morgan Kaufmann},
    title = {Experiments with a new boosting algorithm},
    year = {1996}
 }
 

Valid options are:

 -B <num>
  The number of bins in discretization
  (default 0, no discretization)
 

 -R <num>
  Maximum number of boost iterations.
  (default 10)
 

 -W <class name>
  Full name of classifier to boost.
  eg: weka.classifiers.bayes.NaiveBayes
 

Version:

$Revision: 14512 $

Author:

Eibe Frank (eibe@cs.waikato.ac.nz), Xin Xu (xx5@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors

Constructor and Description
MIBoost()

Method Summary

Modifier and Type	Method and Description
void	buildClassifier(weka.core.Instances exps) Builds the classifier
java.lang.String	discretizeBinTipText() Returns the tip text for this property
double[]	distributionForInstance(weka.core.Instance exmp) Computes the distribution for a given exemplar
weka.core.Capabilities	getCapabilities() Returns default capabilities of the classifier.
int	getDiscretizeBin() Get the number of bins in discretization
int	getMaxIterations() Get the maximum number of boost iterations
weka.core.Capabilities	getMultiInstanceCapabilities() Returns the capabilities of this multi-instance classifier for the relational data.
java.lang.String[]	getOptions() Gets the current settings of the classifier.
java.lang.String	getRevision() Returns the revision string.
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.
java.lang.String	globalInfo() Returns a string describing this filter
java.util.Enumeration<weka.core.Option>	listOptions() Returns an enumeration describing the available options
static void	main(java.lang.String[] argv) Main method for testing this class.
java.lang.String	maxIterationsTipText() Returns the tip text for this property
void	setDiscretizeBin(int bin) Set the number of bins in discretization
void	setMaxIterations(int maxIterations) Set the maximum number of boost iterations
void	setOptions(java.lang.String[] options) Parses a given list of options.
java.lang.String	toString() Gets a string describing the classifier.

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

classifierTipText, getClassifier, postExecution, preExecution, setClassifier

Methods inherited from class weka.classifiers.AbstractClassifier

batchSizeTipText, classifyInstance, debugTipText, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object

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

Methods inherited from interface weka.core.OptionHandler

makeCopy

Constructor Detail

MIBoost

public MIBoost()

Method Detail

globalInfo

public java.lang.String globalInfo()

Returns a string describing this filter

Returns:

a description of the filter 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<weka.core.Option> listOptions()

Returns an enumeration describing the available options

Specified by:

listOptions in interface weka.core.OptionHandler

Overrides:

listOptions in class weka.classifiers.SingleClassifierEnhancer

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:

 -B <num>
  The number of bins in discretization
  (default 0, no discretization)
 

 -R <num>
  Maximum number of boost iterations.
  (default 10)
 

 -W <class name>
  Full name of classifier to boost.
  eg: weka.classifiers.bayes.NaiveBayes
 

Specified by:

setOptions in interface weka.core.OptionHandler

Overrides:

setOptions in class weka.classifiers.SingleClassifierEnhancer

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.SingleClassifierEnhancer

Returns:

an array of strings suitable for passing to setOptions

maxIterationsTipText

public java.lang.String maxIterationsTipText()

Returns the tip text for this property

Returns:

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

setMaxIterations

public void setMaxIterations(int maxIterations)

Set the maximum number of boost iterations

Parameters:

maxIterations - the maximum number of boost iterations

getMaxIterations

public int getMaxIterations()

Get the maximum number of boost iterations

Returns:

the maximum number of boost iterations

discretizeBinTipText

public java.lang.String discretizeBinTipText()

Returns the tip text for this property

Returns:

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

setDiscretizeBin

public void setDiscretizeBin(int bin)

Set the number of bins in discretization

Parameters:

bin - the number of bins in discretization

getDiscretizeBin

public int getDiscretizeBin()

Get the number of bins in discretization

Returns:

the number of bins in discretization

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

getMultiInstanceCapabilities

public weka.core.Capabilities getMultiInstanceCapabilities()

Returns the capabilities of this multi-instance classifier for the relational data.

Specified by:

getMultiInstanceCapabilities in interface weka.core.MultiInstanceCapabilitiesHandler

Returns:

the capabilities of this object

See Also:

Capabilities

buildClassifier

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

Builds the classifier

Specified by:

buildClassifier in interface weka.classifiers.Classifier

Parameters:

exps - 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 exmp)
                                 throws java.lang.Exception

Computes the distribution for a given exemplar

Specified by:

distributionForInstance in interface weka.classifiers.Classifier

Overrides:

distributionForInstance in class weka.classifiers.AbstractClassifier

Parameters:

exmp - the exemplar for which distribution is computed

Returns:

the classification

Throws:

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

toString

public java.lang.String toString()

Gets a string describing the classifier.

Overrides:

toString in class java.lang.Object

Returns:

a string describing the classifer built.

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 - should contain the command line arguments to the scheme (see Evaluation)