Package weka.classifiers.mi
Class MIBoost
- java.lang.Object
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- weka.classifiers.Classifier
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- weka.classifiers.SingleClassifierEnhancer
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- weka.classifiers.mi.MIBoost
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- All Implemented Interfaces:
java.io.Serializable
,java.lang.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:@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:
- Serialized Form
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Constructor Summary
Constructors Constructor Description MIBoost()
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description void
buildClassifier(Instances exps)
Builds the classifierjava.lang.String
discretizeBinTipText()
Returns the tip text for this propertydouble[]
distributionForInstance(Instance exmp)
Computes the distribution for a given exemplarCapabilities
getCapabilities()
Returns default capabilities of the classifier.int
getDiscretizeBin()
Get the number of bins in discretizationint
getMaxIterations()
Get the maximum number of boost iterationsCapabilities
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.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 filterjava.util.Enumeration
listOptions()
Returns an enumeration describing the available optionsstatic void
main(java.lang.String[] argv)
Main method for testing this class.java.lang.String
maxIterationsTipText()
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(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, 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 this filter- Returns:
- a description of the filter 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 classSingleClassifierEnhancer
- 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:-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:
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 classSingleClassifierEnhancer
- Returns:
- an array of strings suitable for passing to setOptions
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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
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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
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
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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
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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getMultiInstanceCapabilities
public Capabilities 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:
Capabilities
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buildClassifier
public void buildClassifier(Instances exps) throws java.lang.Exception
Builds the classifier- Specified by:
buildClassifier
in classClassifier
- 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
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distributionForInstance
public double[] distributionForInstance(Instance exmp) throws java.lang.Exception
Computes the distribution for a given exemplar- Overrides:
distributionForInstance
in classClassifier
- Parameters:
exmp
- the exemplar for which distribution is computed- Returns:
- the classification
- Throws:
java.lang.Exception
- if the distribution can't be computed successfully
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toString
public java.lang.String toString()
Gets a string describing the classifier.- Overrides:
toString
in classjava.lang.Object
- Returns:
- a string describing the classifer built.
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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
- should contain the command line arguments to the scheme (see Evaluation)
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