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: 

 @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:

  • Constructor Details

    • MIBoost

      public MIBoost()

  • Method Details

    • globalInfo

      public String globalInfo()
      Returns a string describing this filter
      Returns:
      a description of the filter suitable for displaying in the explorer/experimenter gui

    • 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 interface TechnicalInformationHandler
      Returns:
      the technical information about this class

    • listOptions

      public Enumeration listOptions()
      Returns an enumeration describing the available options
      Specified by:
      listOptions in interface OptionHandler
      Overrides:
      listOptions in class SingleClassifierEnhancer
      Returns:
      an enumeration of all the available options

    • setOptions

      public void setOptions(String[] options) throws 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 interface OptionHandler
      Overrides:
      setOptions in class SingleClassifierEnhancer
      Parameters:
      options - the list of options as an array of strings
      Throws:
      Exception - if an option is not supported

    • getOptions

      public String[] getOptions()
      Gets the current settings of the classifier.
      Specified by:
      getOptions in interface OptionHandler
      Overrides:
      getOptions in class SingleClassifierEnhancer
      Returns:
      an array of strings suitable for passing to setOptions

    • maxIterationsTipText

      public 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 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 Capabilities getCapabilities()
      Returns default capabilities of the classifier.
      Specified by:
      getCapabilities in interface CapabilitiesHandler
      Overrides:
      getCapabilities in class SingleClassifierEnhancer
      Returns:
      the capabilities of this classifier
      See Also:

    • getMultiInstanceCapabilities

      public Capabilities getMultiInstanceCapabilities()
      Returns the capabilities of this multi-instance classifier for the relational data.
      Specified by:
      getMultiInstanceCapabilities in interface MultiInstanceCapabilitiesHandler
      Returns:
      the capabilities of this object
      See Also:

    • buildClassifier

      public void buildClassifier(Instances exps) throws Exception
      Builds the classifier
      Specified by:
      buildClassifier in class Classifier
      Parameters:
      exps - the training data to be used for generating the boosted classifier.
      Throws:
      Exception - if the classifier could not be built successfully

    • distributionForInstance

      public double[] distributionForInstance(Instance exmp) throws Exception
      Computes the distribution for a given exemplar
      Overrides:
      distributionForInstance in class Classifier
      Parameters:
      exmp - the exemplar for which distribution is computed
      Returns:
      the classification
      Throws:
      Exception - if the distribution can't be computed successfully

    • toString

      public String toString()
      Gets a string describing the classifier.
      Overrides:
      toString in class Object
      Returns:
      a string describing the classifer built.

    • getRevision

      public String getRevision()
      Returns the revision string.
      Specified by:
      getRevision in interface RevisionHandler
      Overrides:
      getRevision in class Classifier
      Returns:
      the revision

    • main

      public static void main(String[] argv)
      Main method for testing this class.
      Parameters:
      argv - should contain the command line arguments to the scheme (see Evaluation)