Package weka.classifiers.lazy

Class KStar

java.lang.Object
weka.classifiers.AbstractClassifier
weka.classifiers.lazy.KStar
All Implemented Interfaces:
Serializable, Cloneable, Classifier, KStarConstants, UpdateableClassifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, TechnicalInformationHandler
public class KStar extends AbstractClassifier implements KStarConstants, UpdateableClassifier, TechnicalInformationHandler
K* is an instance-based classifier, that is the class of a test instance is based upon the class of those training instances similar to it, as determined by some similarity function. It differs from other instance-based learners in that it uses an entropy-based distance function.

For more information on K*, see

John G. Cleary, Leonard E. Trigg: K*: An Instance-based Learner Using an Entropic Distance Measure. In: 12th International Conference on Machine Learning, 108-114, 1995.

BibTeX: 

 @inproceedings{Cleary1995,
    author = {John G. Cleary and Leonard E. Trigg},
    booktitle = {12th International Conference on Machine Learning},
    pages = {108-114},
    title = {K*: An Instance-based Learner Using an Entropic Distance Measure},
    year = {1995}
 }

Valid options are: 

 -B <num>
  Manual blend setting (default 20%)
 
 -E
  Enable entropic auto-blend setting (symbolic class only)
 
 -M <char>
  Specify the missing value treatment mode (default a)
  Valid options are: a(verage), d(elete), m(axdiff), n(ormal)
Version:
$Revision: 15519 $
Author:
Len Trigg (len@reeltwo.com), Abdelaziz Mahoui (am14@cs.waikato.ac.nz) - Java port
See Also:

  • Field Details

    • TAGS_MISSING

      public static final Tag[] TAGS_MISSING
      Define possible missing value handling methods

  • Constructor Details

    • KStar

      public KStar()

  • Method Details

    • globalInfo

      public String globalInfo()
      Returns a string describing classifier
      Returns:
      a description 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

    • getCapabilities

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

    • buildClassifier

      public void buildClassifier(Instances instances) throws Exception
      Generates the classifier.
      Specified by:
      buildClassifier in interface Classifier
      Parameters:
      instances - set of instances serving as training data
      Throws:
      Exception - if the classifier has not been generated successfully

    • updateClassifier

      public void updateClassifier(Instance instance) throws Exception
      Adds the supplied instance to the training set
      Specified by:
      updateClassifier in interface UpdateableClassifier
      Parameters:
      instance - the instance to add
      Throws:
      Exception - if instance could not be incorporated successfully

    • distributionForInstance

      public double[] distributionForInstance(Instance instance) throws Exception
      Calculates the class membership probabilities for the given test instance.
      Specified by:
      distributionForInstance in interface Classifier
      Overrides:
      distributionForInstance in class AbstractClassifier
      Parameters:
      instance - the instance to be classified
      Returns:
      predicted class probability distribution
      Throws:
      Exception - if an error occurred during the prediction

    • missingModeTipText

      public String missingModeTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • getMissingMode

      public SelectedTag getMissingMode()
      Gets the method to use for handling missing values. Will be one of M_NORMAL, M_AVERAGE, M_MAXDIFF or M_DELETE.
      Returns:
      the method used for handling missing values.

    • setMissingMode

      public void setMissingMode(SelectedTag newMode)
      Sets the method to use for handling missing values. Values other than M_NORMAL, M_AVERAGE, M_MAXDIFF and M_DELETE will be ignored.
      Parameters:
      newMode - the method to use for handling missing values.

    • listOptions

      public Enumeration<Option> listOptions()
      Returns an enumeration describing the available options.
      Specified by:
      listOptions in interface OptionHandler
      Overrides:
      listOptions in class AbstractClassifier
      Returns:
      an enumeration of all the available options.

    • globalBlendTipText

      public String globalBlendTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • setGlobalBlend

      public void setGlobalBlend(int b)
      Set the global blend parameter
      Parameters:
      b - the value for global blending

    • getGlobalBlend

      public int getGlobalBlend()
      Get the value of the global blend parameter
      Returns:
      the value of the global blend parameter

    • entropicAutoBlendTipText

      public String entropicAutoBlendTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • setEntropicAutoBlend

      public void setEntropicAutoBlend(boolean e)
      Set whether entropic blending is to be used.
      Parameters:
      e - true if entropic blending is to be used

    • getEntropicAutoBlend

      public boolean getEntropicAutoBlend()
      Get whether entropic blending being used
      Returns:
      true if entropic blending is used

    • setOptions

      public void setOptions(String[] options) throws Exception
      Parses a given list of options.

      Valid options are: 

       -B <num>
  Manual blend setting (default 20%)
 
       -E
  Enable entropic auto-blend setting (symbolic class only)
 
       -M <char>
  Specify the missing value treatment mode (default a)
  Valid options are: a(verage), d(elete), m(axdiff), n(ormal)
      Specified by:
      setOptions in interface OptionHandler
      Overrides:
      setOptions in class AbstractClassifier
      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 K*.
      Specified by:
      getOptions in interface OptionHandler
      Overrides:
      getOptions in class AbstractClassifier
      Returns:
      an array of strings suitable for passing to setOptions()

    • toString

      public String toString()
      Returns a description of this classifier.
      Overrides:
      toString in class Object
      Returns:
      a description of this classifier as a string.

    • main

      public static void main(String[] argv)
      Main method for testing this class.
      Parameters:
      argv - should contain command line options (see setOptions)

    • getRevision

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