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Class weka.classifiers.kstar.KStar

java.lang.Object
   |
   +----weka.classifiers.Classifier
           |
           +----weka.classifiers.DistributionClassifier
                   |
                   +----weka.classifiers.kstar.KStar

public class KStar
extends DistributionClassifier
implements KStarConstants, OptionHandler, UpdateableClassifier, WeightedInstancesHandler
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. The underlying assumption of instance-based classifiers such as K*, IB1, PEBLS, etc, is that similar instances will have similar classes. For more information on K*, see

John, G. Cleary and Leonard, E. Trigg (1995) "K*: An Instance- based Learner Using an Entropic Distance Measure", Proceedings of the 12th International Conference on Machine learning, pp. 108-114.

Author:
Len Trigg (len@intelligenesis.net), Abdelaziz Mahoui (am14@cs.waikato.ac.nz)

Variable Index

 o TAGS_MISSING
Define possible missing value handling methods

Constructor Index

 o KStar()

Method Index

 o buildClassifier(Instances)
Generates the classifier.
 o distributionForInstance(Instance)
Calculates the class membership probabilities for the given test instance.
 o getEntropicAutoBlend()
Get whether entropic blending being used
 o getGlobalBlend()
Get the value of the global blend parameter
 o getMissingMode()
Gets the method to use for handling missing values.
 o getOptions()
Gets the current settings of K*.
 o listOptions()
Returns an enumeration describing the available options
 o main(String[])
Main method for testing this class.
 o setEntropicAutoBlend(boolean)
Set whether entropic blending is to be used.
 o setGlobalBlend(int)
Set the global blend parameter
 o setMissingMode(SelectedTag)
Sets the method to use for handling missing values.
 o setOptions(String[])
Parses a given list of options.
 o toString()
Returns a description of this classifier.
 o updateClassifier(Instance)
Adds the supplied instance to the training set

Variables

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

Constructors

 o KStar
 public KStar()

Methods

 o buildClassifier
 public void buildClassifier(Instances instances) throws Exception
Generates the classifier.

Parameters:
instances - set of instances serving as training data
Throws: Exception
if the classifier has not been generated successfully
Overrides:
buildClassifier in class Classifier
 o updateClassifier
 public void updateClassifier(Instance instance) throws Exception
Adds the supplied instance to the training set

Parameters:
instance - the instance to add
Throws: Exception
if instance could not be incorporated successfully
 o distributionForInstance
 public double[] distributionForInstance(Instance instance) throws Exception
Calculates the class membership probabilities for the given test instance.

Parameters:
instance - the instance to be classified
Returns:
predicted class probability distribution
Throws: Exception
if an error occurred during the prediction
Overrides:
distributionForInstance in class DistributionClassifier
 o 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.
 o 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.
 o listOptions
 public Enumeration listOptions()
Returns an enumeration describing the available options

Returns:
an enumeration of all the available options
 o setGlobalBlend
 public void setGlobalBlend(int b)
Set the global blend parameter

Parameters:
b - the value for global blending
 o getGlobalBlend
 public int getGlobalBlend()
Get the value of the global blend parameter

Returns:
the value of the global blend parameter
 o setEntropicAutoBlend
 public void setEntropicAutoBlend(boolean e)
Set whether entropic blending is to be used.

Parameters:
e - true if entropic blending is to be used
 o getEntropicAutoBlend
 public boolean getEntropicAutoBlend()
Get whether entropic blending being used

Returns:
true if entropic blending is used
 o setOptions
 public void setOptions(String options[]) throws Exception
Parses a given list of options. Valid options are: ...

Parameters:
options - the list of options as an array of strings
Throws: Exception
if an option is not supported
 o getOptions
 public String[] getOptions()
Gets the current settings of K*.

Returns:
an array of strings suitable for passing to setOptions()
 o toString
 public String toString()
Returns a description of this classifier.

Returns:
a description of this classifier as a string.
Overrides:
toString in class Object
 o main
 public static void main(String argv[])
Main method for testing this class.

Parameters:
argv - should contain command line options (see setOptions)

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