Package weka.classifiers.mi
Class MIBoost
java.lang.Object
weka.classifiers.Classifier
weka.classifiers.SingleClassifierEnhancer
weka.classifiers.mi.MIBoost
- All Implemented Interfaces:
Serializable
,Cloneable
,CapabilitiesHandler
,MultiInstanceCapabilitiesHandler
,OptionHandler
,RevisionHandler
,TechnicalInformationHandler
public class MIBoost
extends SingleClassifierEnhancer
implements OptionHandler, MultiInstanceCapabilitiesHandler, 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:
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:
-D Turn on debugging output.
-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
-D If set, classifier is run in debug mode and may output additional info to the console
- Version:
- $Revision: 9144 $
- Author:
- Eibe Frank (eibe@cs.waikato.ac.nz), Xin Xu (xx5@cs.waikato.ac.nz)
- See Also:
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionvoid
buildClassifier
(Instances exps) Builds the classifierReturns the tip text for this propertydouble[]
Computes the distribution for a given exemplarReturns default capabilities of the classifier.int
Get the number of bins in discretizationint
Get the maximum number of boost iterationsReturns the capabilities of this multi-instance classifier for the relational data.String[]
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 this filterReturns an enumeration describing the available optionsstatic void
Main method for testing this class.Returns the tip text for this propertyvoid
setDiscretizeBin
(int bin) Set the number of bins in discretizationvoid
setMaxIterations
(int maxIterations) Set the maximum number of boost iterationsvoid
setOptions
(String[] options) Parses a given list of options.toString()
Gets a string describing the classifier.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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MIBoost
public MIBoost()
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Method Details
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globalInfo
Returns a string describing this filter- Returns:
- a description of the filter 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
- 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 classSingleClassifierEnhancer
- Returns:
- an enumeration of all the available options
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setOptions
Parses a given list of options. Valid options are:-D Turn on debugging output.
-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
-D If set, classifier is run in debug mode and may output additional info to the console
- Specified by:
setOptions
in interfaceOptionHandler
- Overrides:
setOptions
in classSingleClassifierEnhancer
- 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 classSingleClassifierEnhancer
- Returns:
- an array of strings suitable for passing to setOptions
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maxIterationsTipText
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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setMaxIterations
public void setMaxIterations(int maxIterations) Set the maximum number of boost iterations- Parameters:
maxIterations
- the maximum number of boost iterations
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getMaxIterations
public int getMaxIterations()Get the maximum number of boost iterations- Returns:
- the maximum number of boost iterations
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discretizeBinTipText
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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setDiscretizeBin
public void setDiscretizeBin(int bin) Set the number of bins in discretization- Parameters:
bin
- the number of bins in discretization
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getDiscretizeBin
public int getDiscretizeBin()Get the number of bins in discretization- Returns:
- the number of bins in discretization
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getCapabilities
Returns default capabilities of the classifier.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classSingleClassifierEnhancer
- Returns:
- the capabilities of this classifier
- See Also:
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getMultiInstanceCapabilities
Returns the capabilities of this multi-instance classifier for the relational data.- Specified by:
getMultiInstanceCapabilities
in interfaceMultiInstanceCapabilitiesHandler
- Returns:
- the capabilities of this object
- See Also:
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buildClassifier
Builds the classifier- Specified by:
buildClassifier
in classClassifier
- Parameters:
exps
- the training data to be used for generating the boosted classifier.- Throws:
Exception
- if the classifier could not be built successfully
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distributionForInstance
Computes the distribution for a given exemplar- Overrides:
distributionForInstance
in classClassifier
- Parameters:
exmp
- the exemplar for which distribution is computed- Returns:
- the classification
- Throws:
Exception
- if the distribution can't be computed successfully
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toString
Gets a string describing the classifier. -
getRevision
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classClassifier
- Returns:
- the revision
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main
Main method for testing this class.- Parameters:
argv
- should contain the command line arguments to the scheme (see Evaluation)
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