Package weka.classifiers.meta
Class MultiBoostAB
- All Implemented Interfaces:
Serializable
,Cloneable
,Sourcable
,CapabilitiesHandler
,OptionHandler
,Randomizable
,RevisionHandler
,TechnicalInformationHandler
,WeightedInstancesHandler
Class for boosting a classifier using the MultiBoosting method.
MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees. MultiBoosting can be viewed as combining AdaBoost with wagging. It is able to harness both AdaBoost's high bias and variance reduction with wagging's superior variance reduction. Using C4.5 as the base learning algorithm, Multi-boosting is demonstrated to produce decision committees with lower error than either AdaBoost or wagging significantly more often than the reverse over a large representative cross-section of UCI data sets. It offers the further advantage over AdaBoost of suiting parallel execution.
For more information, see
Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2). BibTeX:
MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees. MultiBoosting can be viewed as combining AdaBoost with wagging. It is able to harness both AdaBoost's high bias and variance reduction with wagging's superior variance reduction. Using C4.5 as the base learning algorithm, Multi-boosting is demonstrated to produce decision committees with lower error than either AdaBoost or wagging significantly more often than the reverse over a large representative cross-section of UCI data sets. It offers the further advantage over AdaBoost of suiting parallel execution.
For more information, see
Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2). BibTeX:
@article{Webb2000, address = {Boston}, author = {Geoffrey I. Webb}, journal = {Machine Learning}, number = {No.2}, publisher = {Kluwer Academic Publishers}, title = {MultiBoosting: A Technique for Combining Boosting and Wagging}, volume = {Vol.40}, year = {2000} }Valid options are:
-C <num> Number of sub-committees. (Default 3)
-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 consoleOptions after -- are passed to the designated classifier.
- Version:
- $Revision: 1.16 $
- Author:
- Shane Butler (sbutle@deakin.edu.au), Eibe Frank (eibe@cs.waikato.ac.nz), Len Trigg (trigg@cs.waikato.ac.nz)
- See Also:
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionvoid
buildClassifier
(Instances training) Method for building this classifier.int
Get the number of sub committees to useString[]
Gets the current settings of the Classifier.Returns the revision string.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.Returns a string describing classifierReturns an enumeration describing the available optionsstatic void
Main method for testing this class.Returns the tip text for this propertyvoid
setNumSubCmtys
(int subc) Set the number of sub committees to usevoid
setOptions
(String[] options) Parses a given list of options.toString()
Returns description of the boosted classifier.Methods inherited from class weka.classifiers.meta.AdaBoostM1
distributionForInstance, getCapabilities, getUseResampling, getWeightThreshold, setUseResampling, setWeightThreshold, toSource, useResamplingTipText, weightThresholdTipText
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, makeCopy, setDebug
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Constructor Details
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MultiBoostAB
public MultiBoostAB()
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Method Details
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globalInfo
Returns a string describing classifier- Overrides:
globalInfo
in classAdaBoostM1
- Returns:
- a description suitable for displaying in the explorer/experimenter gui
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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 interfaceTechnicalInformationHandler
- Overrides:
getTechnicalInformation
in classAdaBoostM1
- Returns:
- the technical information about this class
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listOptions
Returns an enumeration describing the available options- Specified by:
listOptions
in interfaceOptionHandler
- Overrides:
listOptions
in classAdaBoostM1
- Returns:
- an enumeration of all the available options
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setOptions
Parses a given list of options. Valid options are:-C <num> Number of sub-committees. (Default 3)
-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 interfaceOptionHandler
- Overrides:
setOptions
in classAdaBoostM1
- Parameters:
options
- the list of options as an array of strings- Throws:
Exception
- if an option is not supported
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getOptions
Gets the current settings of the Classifier.- Specified by:
getOptions
in interfaceOptionHandler
- Overrides:
getOptions
in classAdaBoostM1
- Returns:
- an array of strings suitable for passing to setOptions
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numSubCmtysTipText
Returns the tip text for this property- Returns:
- tip text for this property suitable for displaying in the explorer/experimenter gui
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setNumSubCmtys
public void setNumSubCmtys(int subc) Set the number of sub committees to use- Parameters:
subc
- the number of sub committees
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getNumSubCmtys
public int getNumSubCmtys()Get the number of sub committees to use- Returns:
- the seed for resampling
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buildClassifier
Method for building this classifier.- Overrides:
buildClassifier
in classAdaBoostM1
- Parameters:
training
- the data to train with- Throws:
Exception
- if the training fails
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toString
Returns description of the boosted classifier.- Overrides:
toString
in classAdaBoostM1
- Returns:
- description of the boosted classifier as a string
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getRevision
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classAdaBoostM1
- Returns:
- the revision
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main
Main method for testing this class.- Parameters:
argv
- the options
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