weka.classifiers.lazy
Class KStar

java.lang.Object
  weka.classifiers.AbstractClassifier
      weka.classifiers.lazy.KStar

All Implemented Interfaces:

Serializable, Cloneable, Classifier, KStarConstants, UpdateableClassifier, CapabilitiesHandler, 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: 8034 $

Author:

Len Trigg ([email protected]), Abdelaziz Mahoui ([email protected]) - Java port

See Also:

Serialized Form

Field Summary

static Tag[]

TAGS_MISSING Define possible missing value handling methods

Fields inherited from interface weka.classifiers.lazy.kstar.KStarConstants

B_ENTROPY, B_SPHERE, EPSILON, FLOOR, FLOOR1, INITIAL_STEP, LOG2, M_AVERAGE, M_DELETE, M_MAXDIFF, M_NORMAL, NUM_RAND_COLS, OFF, ON, ROOT_FINDER_ACCURACY, ROOT_FINDER_MAX_ITER

Constructor Summary

KStar()

Method Summary

void	buildClassifier(Instances instances) Generates the classifier.
double[]	distributionForInstance(Instance instance) Calculates the class membership probabilities for the given test instance.
String	entropicAutoBlendTipText() Returns the tip text for this property
Capabilities	getCapabilities() Returns default capabilities of the classifier.
boolean	getEntropicAutoBlend() Get whether entropic blending being used
int	getGlobalBlend() Get the value of the global blend parameter
SelectedTag	getMissingMode() Gets the method to use for handling missing values.
String[]	getOptions() Gets the current settings of K*.
String	getRevision() Returns the revision string.
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.
String	globalBlendTipText() Returns the tip text for this property
String	globalInfo() Returns a string describing classifier
Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(String[] argv) Main method for testing this class.
String	missingModeTipText() Returns the tip text for this property
void	setEntropicAutoBlend(boolean e) Set whether entropic blending is to be used.
void	setGlobalBlend(int b) Set the global blend parameter
void	setMissingMode(SelectedTag newMode) Sets the method to use for handling missing values.
void	setOptions(String[] options) Parses a given list of options.
String	toString() Returns a description of this classifier.
void	updateClassifier(Instance instance) Adds the supplied instance to the training set

Methods inherited from class weka.classifiers.AbstractClassifier

classifyInstance, debugTipText, forName, getDebug, makeCopies, makeCopy, runClassifier, setDebug

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

TAGS_MISSING

public static final Tag[] TAGS_MISSING

Define possible missing value handling methods

Constructor Detail

KStar

public KStar()

Method Detail

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 Classifier

Specified by:

getCapabilities in interface CapabilitiesHandler

Overrides:

getCapabilities in class AbstractClassifier

Returns:

the capabilities of this classifier

See Also:

Capabilities

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 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