weka.classifiers.meta

Class RandomSubSpace

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.SingleClassifierEnhancer

weka.classifiers.IteratedSingleClassifierEnhancer

weka.classifiers.ParallelIteratedSingleClassifierEnhancer

weka.classifiers.RandomizableParallelIteratedSingleClassifierEnhancer

weka.classifiers.meta.RandomSubSpace

All Implemented Interfaces:

java.io.Serializable, java.lang.Cloneable, Classifier, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class RandomSubSpace
extends RandomizableParallelIteratedSingleClassifierEnhancer
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: 15801 $

Author:

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

See Also:

Serialized Form

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors

Constructor and Description
RandomSubSpace() Constructor.

Method Summary

Modifier and Type	Method and Description
java.lang.String	batchSizeTipText() Tool tip text for this property
void	buildClassifier(Instances data) builds the classifier.
double[]	distributionForInstance(Instance instance) Calculates the class membership probabilities for the given test instance.
double[][]	distributionsForInstances(Instances insts) Batch scoring method.
java.lang.String	getBatchSize() Gets the preferred batch size from the base learner if it implements BatchPredictor.
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
boolean	implementsMoreEfficientBatchPrediction() Returns true if the base classifier implements BatchPredictor and is able to generate batch predictions efficiently
java.util.Enumeration<Option>	listOptions() Returns an enumeration describing the available options.
static void	main(java.lang.String[] args) Main method for testing this class.
void	setBatchSize(java.lang.String size) Set the batch size to use.
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.RandomizableParallelIteratedSingleClassifierEnhancer

getSeed, seedTipText, setSeed

Methods inherited from class weka.classifiers.ParallelIteratedSingleClassifierEnhancer

getNumExecutionSlots, numExecutionSlotsTipText, setNumExecutionSlots

Methods inherited from class weka.classifiers.IteratedSingleClassifierEnhancer

getNumIterations, numIterationsTipText, setNumIterations

Methods inherited from class weka.classifiers.SingleClassifierEnhancer

classifierTipText, getCapabilities, getClassifier, postExecution, preExecution, setClassifier

Methods inherited from class weka.classifiers.AbstractClassifier

classifyInstance, debugTipText, doNotCheckCapabilitiesTipText, forName, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, makeCopies, makeCopy, numDecimalPlacesTipText, run, runClassifier, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

Methods inherited from class java.lang.Object

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

Methods inherited from interface weka.core.OptionHandler

makeCopy

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<Option> listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class RandomizableParallelIteratedSingleClassifierEnhancer

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 RandomizableParallelIteratedSingleClassifierEnhancer

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 RandomizableParallelIteratedSingleClassifierEnhancer

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.

Specified by:

buildClassifier in interface Classifier

Overrides:

buildClassifier in class ParallelIteratedSingleClassifierEnhancer

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.

Specified by:

distributionForInstance in interface Classifier

Overrides:

distributionForInstance in class AbstractClassifier

Parameters:

instance - the instance to be classified

Returns:

preedicted class probability distribution

Throws:

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

batchSizeTipText

public java.lang.String batchSizeTipText()

Tool tip text for this property

Overrides:

batchSizeTipText in class AbstractClassifier

Returns:

the tool tip for this property

setBatchSize

public void setBatchSize(java.lang.String size)

Set the batch size to use. Gets passed through to the base learner if it implements BatchPredictor. Otherwise it is just ignored.

Specified by:

setBatchSize in interface BatchPredictor

Overrides:

setBatchSize in class AbstractClassifier

Parameters:

size - the batch size to use

getBatchSize

public java.lang.String getBatchSize()

Gets the preferred batch size from the base learner if it implements BatchPredictor. Returns 1 as the preferred batch size otherwise.

Specified by:

getBatchSize in interface BatchPredictor

Overrides:

getBatchSize in class AbstractClassifier

Returns:

the batch size to use

distributionsForInstances

public double[][] distributionsForInstances(Instances insts)
                                     throws java.lang.Exception

Batch scoring method. Calls the appropriate method for the base learner if it implements BatchPredictor. Otherwise it simply calls the distributionForInstance() method repeatedly.

Specified by:

distributionsForInstances in interface BatchPredictor

Overrides:

distributionsForInstances in class AbstractClassifier

Parameters:

insts - the instances to get predictions for

Returns:

an array of probability distributions, one for each instance

Throws:

java.lang.Exception - if a problem occurs

implementsMoreEfficientBatchPrediction

public boolean implementsMoreEfficientBatchPrediction()

Returns true if the base classifier implements BatchPredictor and is able to generate batch predictions efficiently

Specified by:

implementsMoreEfficientBatchPrediction in interface BatchPredictor

Overrides:

implementsMoreEfficientBatchPrediction in class AbstractClassifier

Returns:

true if the base classifier can generate batch predictions efficiently

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 AbstractClassifier

Returns:

the revision

main

public static void main(java.lang.String[] args)

Main method for testing this class.

Parameters:

args - the options