Class RandomSubSpace

All Implemented Interfaces:
Serializable, Cloneable, CapabilitiesHandler, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

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:
  • 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 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(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 RandomizableIteratedSingleClassifierEnhancer
      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 RandomizableIteratedSingleClassifierEnhancer
      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.
      Overrides:
      buildClassifier in class IteratedSingleClassifierEnhancer
      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.
      Overrides:
      distributionForInstance in class Classifier
      Parameters:
      instance - the instance to be classified
      Returns:
      preedicted class probability distribution
      Throws:
      Exception - if distribution can't be computed successfully
    • 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 Classifier
      Returns:
      the revision
    • main

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