Package weka.classifiers.meta

Class 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:
    $Revision$
    Author:
    Bernhard Pfahringer (bernhard@cs.waikato.ac.nz), Peter Reutemann (fracpete@cs.waikato.ac.nz)
    See Also:
    Serialized Form

    • Constructor Detail

      • RandomSubSpace

        public RandomSubSpace()
        Constructor.

    • 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

      • 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

      • 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.

      • 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

      • toString

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

      • main

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