Package weka.classifiers.mi

Class MIBoost

  • 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

    • Constructor Detail

      • MIBoost

        public MIBoost()

    • Method Detail

      • 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

      • 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

      • 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 interface OptionHandler
        Overrides:
        setOptions in class SingleClassifierEnhancer
        Parameters:
        options - the list of options as an array of strings
        Throws:
        java.lang.Exception - if an option is not supported

      • 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

      • 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 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

      • 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

      • buildClassifier

        public void buildClassifier​(Instances exps)
                     throws java.lang.Exception
        Builds the classifier
        Specified by:
        buildClassifier in class Classifier
        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

      • distributionForInstance

        public double[] distributionForInstance​(Instance exmp)
                                 throws java.lang.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:
        java.lang.Exception - if the distribution can't be computed successfully

      • toString

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

      • 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)