weka.classifiers.meta

Class RandomSubSpace

java.lang.Object

weka.classifiers.Classifier

weka.classifiers.SingleClassifierEnhancer

weka.classifiers.IteratedSingleClassifierEnhancer

weka.classifiers.RandomizableIteratedSingleClassifierEnhancer

weka.classifiers.meta.RandomSubSpace

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class RandomSubSpace
extends RandomizableIteratedSingleClassifierEnhancer
implements WeightedInstancesHandler, TechnicalInformationHandler

This method constructs a decision tree based classifier that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity. The classifier consists of multiple trees constructed systematically by pseudorandomly selecting subsets of components of the feature vector, that is, trees constructed in randomly chosen subspaces.

For more information, see

Tin Kam Ho (1998). The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence. 20(8):832-844. URL http://citeseer.ist.psu.edu/ho98random.html.

BibTeX:

 @article{Ho1998,
    author = {Tin Kam Ho},
    journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
    number = {8},
    pages = {832-844},
    title = {The Random Subspace Method for Constructing Decision Forests},
    volume = {20},
    year = {1998},
    ISSN = {0162-8828},
    URL = {http://citeseer.ist.psu.edu/ho98random.html}
 }
 

Valid options are:

 -P
  Size of each subspace:
   < 1: percentage of the number of attributes
   >=1: absolute number of attributes
 

 -S <num>
  Random number seed.
  (default 1)

 -I <num>
  Number of iterations.
  (default 10)

 -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.trees.REPTree)

 
 Options specific to classifier weka.classifiers.trees.REPTree:
 

 -M <minimum number of instances>
  Set minimum number of instances per leaf (default 2).

 -V <minimum variance for split>
  Set minimum numeric class variance proportion
  of train variance for split (default 1e-3).

 -N <number of folds>
  Number of folds for reduced error pruning (default 3).

 -S <seed>
  Seed for random data shuffling (default 1).

 -P
  No pruning.

 -L
  Maximum tree depth (default -1, no maximum)

Options after -- are passed to the designated classifier.

Version:

$Revision: 1.4 $

Author:

Bernhard Pfahringer (bernhard@cs.waikato.ac.nz), Peter Reutemann (fracpete@cs.waikato.ac.nz)

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
RandomSubSpace() Constructor.

Method Summary

Modifier and Type	Method and Description
void	buildClassifier(Instances data) builds the classifier.
double[]	distributionForInstance(Instance instance) Calculates the class membership probabilities for the given test instance.
java.lang.String[]	getOptions() Gets the current settings of the Classifier.
java.lang.String	getRevision() Returns the revision string.
double	getSubSpaceSize() Gets the size of each subSpace, as a percentage of the training set size.
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.
java.lang.String	globalInfo() Returns a string describing classifier
java.util.Enumeration	listOptions() Returns an enumeration describing the available options.
static void	main(java.lang.String[] args) Main method for testing this class.
void	setOptions(java.lang.String[] options) Parses a given list of options.
void	setSubSpaceSize(double value) Sets the size of each subSpace, as a percentage of the training set size.
java.lang.String	subSpaceSizeTipText() Returns the tip text for this property
java.lang.String	toString() Returns description of the bagged classifier.

Methods inherited from class weka.classifiers.RandomizableIteratedSingleClassifierEnhancer

getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer

getNumIterations, numIterationsTipText, setNumIterations

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

classifierTipText, getCapabilities, getClassifier, setClassifier

Methods inherited from class weka.classifiers.Classifier

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

Methods inherited from class java.lang.Object

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

Constructor Detail

RandomSubSpace

public RandomSubSpace()

Constructor.

Method Detail

globalInfo

public java.lang.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

listOptions

public java.util.Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class RandomizableIteratedSingleClassifierEnhancer

Returns:

an enumeration of all the available options.

setOptions

public void setOptions(java.lang.String[] options)
                throws java.lang.Exception

Parses a given list of options.

Valid options are:

 -P
  Size of each subspace:
   < 1: percentage of the number of attributes
   >=1: absolute number of attributes
 

 -S <num>
  Random number seed.
  (default 1)

 -I <num>
  Number of iterations.
  (default 10)

 -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.trees.REPTree)

 
 Options specific to classifier weka.classifiers.trees.REPTree:
 

 -M <minimum number of instances>
  Set minimum number of instances per leaf (default 2).

 -V <minimum variance for split>
  Set minimum numeric class variance proportion
  of train variance for split (default 1e-3).

 -N <number of folds>
  Number of folds for reduced error pruning (default 3).

 -S <seed>
  Seed for random data shuffling (default 1).

 -P
  No pruning.

 -L
  Maximum tree depth (default -1, no maximum)

Options after -- are passed to the designated classifier.

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class RandomizableIteratedSingleClassifierEnhancer

Parameters:

options - the list of options as an array of strings

Throws:

java.lang.Exception - if an option is not supported

getOptions

public java.lang.String[] getOptions()

Gets the current settings of the Classifier.

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class RandomizableIteratedSingleClassifierEnhancer

Returns:

an array of strings suitable for passing to setOptions

subSpaceSizeTipText

public java.lang.String subSpaceSizeTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getSubSpaceSize

public double getSubSpaceSize()

Gets the size of each subSpace, as a percentage of the training set size.

Returns:

the subSpace size, as a percentage.

setSubSpaceSize

public void setSubSpaceSize(double value)

Sets the size of each subSpace, as a percentage of the training set size.

Parameters:

value - the subSpace size, as a percentage.

buildClassifier

public void buildClassifier(Instances data)
                     throws java.lang.Exception

builds the classifier.

Overrides:

buildClassifier in class IteratedSingleClassifierEnhancer

Parameters:

data - the training data to be used for generating the classifier.

Throws:

java.lang.Exception - if the classifier could not be built successfully

distributionForInstance

public double[] distributionForInstance(Instance instance)
                                 throws java.lang.Exception

Calculates the class membership probabilities for the given test instance.

Overrides:

distributionForInstance in class Classifier

Parameters:

instance - the instance to be classified

Returns:

preedicted class probability distribution

Throws:

java.lang.Exception - if distribution can't be computed successfully

toString

public java.lang.String toString()

Returns description of the bagged classifier.

Overrides:

toString in class java.lang.Object

Returns:

description of the bagged classifier as a string

getRevision

public java.lang.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(java.lang.String[] args)

Main method for testing this class.

Parameters:

args - the options