Package weka.classifiers.mi

Class MIWrapper

  • All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, MultiInstanceCapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler
    public class MIWrapper
extends SingleClassifierEnhancer
implements MultiInstanceCapabilitiesHandler, OptionHandler, TechnicalInformationHandler
    A simple Wrapper method for applying standard propositional learners to multi-instance data.

    For more information see:

    E. T. Frank, X. Xu (2003). Applying propositional learning algorithms to multi-instance data. Department of Computer Science, University of Waikato, Hamilton, NZ.

    BibTeX: 

     @techreport{Frank2003,
    address = {Department of Computer Science, University of Waikato, Hamilton, NZ},
    author = {E. T. Frank and X. Xu},
    institution = {University of Waikato},
    month = {06},
    title = {Applying propositional learning algorithms to multi-instance data},
    year = {2003}
 }

    Valid options are: 

     -P [1|2|3]
  The method used in testing:
  1.arithmetic average
  2.geometric average
  3.max probability of positive bag.
  (default: 1)
     -A [0|1|2|3]
  The type of weight setting for each single-instance:
  0.keep the weight to be the same as the original value;
  1.weight = 1.0
  2.weight = 1.0/Total number of single-instance in the
   corresponding bag
  3. weight = Total number of single-instance / (Total
   number of bags * Total number of single-instance 
   in the corresponding bag).
  (default: 3)
     -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.rules.ZeroR)
     
 Options specific to classifier weka.classifiers.rules.ZeroR:
 
     -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

    • Field Detail

      • TESTMETHOD_ARITHMETIC

        public static final int TESTMETHOD_ARITHMETIC
        arithmetic average
        See Also:
        Constant Field Values

      • TESTMETHOD_GEOMETRIC

        public static final int TESTMETHOD_GEOMETRIC
        geometric average
        See Also:
        Constant Field Values

      • TESTMETHOD_MAXPROB

        public static final int TESTMETHOD_MAXPROB
        max probability of positive bag
        See Also:
        Constant Field Values

      • TAGS_TESTMETHOD

        public static final Tag[] TAGS_TESTMETHOD
        the test methods

    • Constructor Detail

      • MIWrapper

        public MIWrapper()

    • 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: 

         -P [1|2|3]
  The method used in testing:
  1.arithmetic average
  2.geometric average
  3.max probability of positive bag.
  (default: 1)
         -A [0|1|2|3]
  The type of weight setting for each single-instance:
  0.keep the weight to be the same as the original value;
  1.weight = 1.0
  2.weight = 1.0/Total number of single-instance in the
   corresponding bag
  3. weight = Total number of single-instance / (Total
   number of bags * Total number of single-instance 
   in the corresponding bag).
  (default: 3)
         -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.rules.ZeroR)
         
 Options specific to classifier weka.classifiers.rules.ZeroR:
 
         -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

      • weightMethodTipText

        public java.lang.String weightMethodTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

      • setWeightMethod

        public void setWeightMethod​(SelectedTag method)
        The new method for weighting the instances.
        Parameters:
        method - the new method

      • getWeightMethod

        public SelectedTag getWeightMethod()
        Returns the current weighting method for instances.
        Returns:
        the current weighting method

      • methodTipText

        public java.lang.String methodTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

      • setMethod

        public void setMethod​(SelectedTag method)
        Set the method used in testing.
        Parameters:
        method - the index of method to use.

      • getMethod

        public SelectedTag getMethod()
        Get the method used in testing.
        Returns:
        the index of method used in testing.

      • buildClassifier

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

      • 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 distribution
        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)