Package weka.classifiers.meta
Class AdaBoostM1
- java.lang.Object
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- All Implemented Interfaces:
java.io.Serializable
,java.lang.Cloneable
,Sourcable
,CapabilitiesHandler
,OptionHandler
,Randomizable
,RevisionHandler
,TechnicalInformationHandler
,WeightedInstancesHandler
- Direct Known Subclasses:
MultiBoostAB
public class AdaBoostM1 extends RandomizableIteratedSingleClassifierEnhancer implements WeightedInstancesHandler, Sourcable, TechnicalInformationHandler
Class for boosting a nominal class classifier using the Adaboost M1 method. Only nominal class problems can be tackled. Often dramatically improves performance, but sometimes overfits.
For more information, 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:-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: 1.40 $
- Author:
- Eibe Frank (eibe@cs.waikato.ac.nz), Len Trigg (trigg@cs.waikato.ac.nz)
- See Also:
- Serialized Form
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Constructor Summary
Constructors Constructor Description AdaBoostM1()
Constructor.
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description void
buildClassifier(Instances data)
Boosting method.double[]
distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance.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.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 onint
getWeightThreshold()
Get the degree of weight thresholdingjava.lang.String
globalInfo()
Returns a string describing classifierjava.util.Enumeration
listOptions()
Returns an enumeration describing the available options.static void
main(java.lang.String[] argv)
Main method for testing this class.void
setOptions(java.lang.String[] options)
Parses a given list of options.void
setUseResampling(boolean r)
Set resampling modevoid
setWeightThreshold(int threshold)
Set weight thresholdjava.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 propertyjava.lang.String
weightThresholdTipText()
Returns the tip text for this property-
Methods inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer
getSeed, seedTipText, setSeed
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Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer
getNumIterations, numIterationsTipText, setNumIterations
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Methods inherited from class weka.classifiers.SingleClassifierEnhancer
classifierTipText, getClassifier, setClassifier
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Methods inherited from class weka.classifiers.Classifier
classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, setDebug
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Method Detail
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globalInfo
public java.lang.String globalInfo()
Returns a string describing classifier- Returns:
- a description suitable for displaying in the explorer/experimenter gui
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getTechnicalInformation
public 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 interfaceTechnicalInformationHandler
- Returns:
- the technical information about this class
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listOptions
public java.util.Enumeration listOptions()
Returns an enumeration describing the available options.- Specified by:
listOptions
in interfaceOptionHandler
- Overrides:
listOptions
in classRandomizableIteratedSingleClassifierEnhancer
- Returns:
- an enumeration of all the available options.
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setOptions
public void setOptions(java.lang.String[] options) throws java.lang.Exception
Parses a given list of options. Valid options are:-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 classRandomizableIteratedSingleClassifierEnhancer
- Parameters:
options
- the list of options as an array of strings- Throws:
java.lang.Exception
- if an option is not supported
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getOptions
public java.lang.String[] getOptions()
Gets the current settings of the Classifier.- Specified by:
getOptions
in interfaceOptionHandler
- Overrides:
getOptions
in classRandomizableIteratedSingleClassifierEnhancer
- Returns:
- an array of strings suitable for passing to setOptions
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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
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setWeightThreshold
public void setWeightThreshold(int threshold)
Set weight threshold- Parameters:
threshold
- the percentage of weight mass used for training
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getWeightThreshold
public int getWeightThreshold()
Get the degree of weight thresholding- Returns:
- the percentage of weight mass used for training
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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
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setUseResampling
public void setUseResampling(boolean r)
Set resampling mode- Parameters:
r
- true if resampling should be done
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getUseResampling
public boolean getUseResampling()
Get whether resampling is turned on- Returns:
- true if resampling output is on
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getCapabilities
public Capabilities getCapabilities()
Returns default capabilities of the classifier.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classSingleClassifierEnhancer
- Returns:
- the capabilities of this classifier
- See Also:
Capabilities
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buildClassifier
public void buildClassifier(Instances data) throws java.lang.Exception
Boosting method.- Overrides:
buildClassifier
in classIteratedSingleClassifierEnhancer
- 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
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distributionForInstance
public double[] distributionForInstance(Instance instance) throws java.lang.Exception
Calculates the class membership probabilities for the given test instance.- Overrides:
distributionForInstance
in classClassifier
- Parameters:
instance
- the instance to be classified- Returns:
- predicted class probability distribution
- Throws:
java.lang.Exception
- if instance could not be classified successfully
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toSource
public java.lang.String toSource(java.lang.String className) throws java.lang.Exception
Returns the boosted model as Java source code.
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toString
public java.lang.String toString()
Returns description of the boosted classifier.- Overrides:
toString
in classjava.lang.Object
- Returns:
- description of the boosted classifier as a string
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getRevision
public java.lang.String getRevision()
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classClassifier
- Returns:
- the revision
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main
public static void main(java.lang.String[] argv)
Main method for testing this class.- Parameters:
argv
- the options
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