Package weka.classifiers.meta.nestedDichotomies

Class ND

  • All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, CapabilitiesHandler, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler
    public class ND
extends RandomizableSingleClassifierEnhancer
implements TechnicalInformationHandler
    A meta classifier for handling multi-class datasets with 2-class classifiers by building a random tree structure.

    For more info, check

    Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems. In: PKDD, 84-95, 2005.

    Eibe Frank, Stefan Kramer: Ensembles of nested dichotomies for multi-class problems. In: Twenty-first International Conference on Machine Learning, 2004.

    BibTeX: 

     @inproceedings{Dong2005,
    author = {Lin Dong and Eibe Frank and Stefan Kramer},
    booktitle = {PKDD},
    pages = {84-95},
    publisher = {Springer},
    title = {Ensembles of Balanced Nested Dichotomies for Multi-class Problems},
    year = {2005}
 }
 
 @inproceedings{Frank2004,
    author = {Eibe Frank and Stefan Kramer},
    booktitle = {Twenty-first International Conference on Machine Learning},
    publisher = {ACM},
    title = {Ensembles of nested dichotomies for multi-class problems},
    year = {2004}
 }

    Valid options are: 

     -S <num>
  Random number seed.
  (default 1)
     -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.J48)
     
 Options specific to classifier weka.classifiers.trees.J48:
 
     -U
  Use unpruned tree.
     -C <pruning confidence>
  Set confidence threshold for pruning.
  (default 0.25)
     -M <minimum number of instances>
  Set minimum number of instances per leaf.
  (default 2)
     -R
  Use reduced error pruning.
     -N <number of folds>
  Set number of folds for reduced error
  pruning. One fold is used as pruning set.
  (default 3)
     -B
  Use binary splits only.
     -S
  Don't perform subtree raising.
     -L
  Do not clean up after the tree has been built.
     -A
  Laplace smoothing for predicted probabilities.
     -Q <seed>
  Seed for random data shuffling (default 1).
    Author:
    Eibe Frank, Lin Dong
    See Also:
    Serialized Form

    • Constructor Detail

      • ND

        public ND()
        Constructor.

    • Method Detail

      • 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

      • setHashtable

        public void setHashtable​(java.util.Hashtable table)
        Set hashtable from END.
        Parameters:
        table - the hashtable to use

      • buildClassifier

        public void buildClassifier​(Instances data)
                     throws java.lang.Exception
        Builds the classifier.
        Specified by:
        buildClassifier in class Classifier
        Parameters:
        data - the data to train the classifier with
        Throws:
        java.lang.Exception - if anything goes wrong

      • buildClassifierForNode

        public void buildClassifierForNode​(weka.classifiers.meta.nestedDichotomies.ND.NDTree node,
                                   Instances data)
                            throws java.lang.Exception
        Builds the classifier for one node.
        Parameters:
        node - the node to build the classifier for
        data - the data to work with
        Throws:
        java.lang.Exception - if anything goes wrong

      • distributionForInstance

        public double[] distributionForInstance​(Instance inst)
                                 throws java.lang.Exception
        Predicts the class distribution for a given instance
        Overrides:
        distributionForInstance in class Classifier
        Parameters:
        inst - the (multi-class) instance to be classified
        Returns:
        the class distribution
        Throws:
        java.lang.Exception - if computing fails

      • toString

        public java.lang.String toString()
        Outputs the classifier as a string.
        Overrides:
        toString in class java.lang.Object
        Returns:
        a string representation of the classifier

      • globalInfo

        public java.lang.String globalInfo()
        Returns:
        a description of the classifier suitable for displaying in the explorer/experimenter gui

      • main

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