Package 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:
Serializable, 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: 15800 $
Author:
Bernhard Pfahringer (bernhard@cs.waikato.ac.nz), Peter Reutemann (fracpete@cs.waikato.ac.nz)
See Also:

  • Constructor Details

    • RandomSubSpace

      public RandomSubSpace()
      Constructor.

  • 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

    • listOptions

      public 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(String[] options) throws 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:
      Exception - if an option is not supported

    • getOptions

      public 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 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 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:
      Exception - if the classifier could not be built 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:
      preedicted class probability distribution
      Throws:
      Exception - if distribution can't be computed successfully

    • batchSizeTipText

      public String batchSizeTipText()
      Tool tip text for this property
      Overrides:
      batchSizeTipText in class AbstractClassifier
      Returns:
      the tool tip for this property

    • setBatchSize

      public void setBatchSize(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 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 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:
      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 String toString()
      Returns description of the bagged classifier.
      Overrides:
      toString in class Object
      Returns:
      description of the bagged classifier as a string

    • getRevision

      public 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(String[] args)
      Main method for testing this class.
      Parameters:
      args - the options