Package weka.classifiers.mi

Class MIWrapper

java.lang.Object
weka.classifiers.Classifier
weka.classifiers.SingleClassifierEnhancer
weka.classifiers.mi.MIWrapper
All Implemented Interfaces:
Serializable, 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:

  • Field Details

    • TESTMETHOD_ARITHMETIC

      public static final int TESTMETHOD_ARITHMETIC
      arithmetic average
      See Also:

    • TESTMETHOD_GEOMETRIC

      public static final int TESTMETHOD_GEOMETRIC
      geometric average
      See Also:

    • TESTMETHOD_MAXPROB

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

    • TAGS_TESTMETHOD

      public static final Tag[] TAGS_TESTMETHOD
      the test methods

  • Constructor Details

    • MIWrapper

      public MIWrapper()

  • 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: 

       -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:
      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

    • weightMethodTipText

      public 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 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.

    • 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 data) throws Exception
      Builds the classifier
      Specified by:
      buildClassifier in class Classifier
      Parameters:
      data - 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 distribution
      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)