Package weka.classifiers.rules

Class M5Rules

java.lang.Object
weka.classifiers.AbstractClassifier
weka.classifiers.trees.m5.M5Base
weka.classifiers.rules.M5Rules
All Implemented Interfaces:
Serializable, Cloneable, Classifier, AdditionalMeasureProducer, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, TechnicalInformationHandler
public class M5Rules extends M5Base implements TechnicalInformationHandler
Generates a decision list for regression problems using separate-and-conquer. In each iteration it builds a model tree using M5 and makes the "best" leaf into a rule.

For more information see:

Geoffrey Holmes, Mark Hall, Eibe Frank: Generating Rule Sets from Model Trees. In: Twelfth Australian Joint Conference on Artificial Intelligence, 1-12, 1999.

Ross J. Quinlan: Learning with Continuous Classes. In: 5th Australian Joint Conference on Artificial Intelligence, Singapore, 343-348, 1992.

Y. Wang, I. H. Witten: Induction of model trees for predicting continuous classes. In: Poster papers of the 9th European Conference on Machine Learning, 1997.

BibTeX: 

 @inproceedings{Holmes1999,
    author = {Geoffrey Holmes and Mark Hall and Eibe Frank},
    booktitle = {Twelfth Australian Joint Conference on Artificial Intelligence},
    pages = {1-12},
    publisher = {Springer},
    title = {Generating Rule Sets from Model Trees},
    year = {1999}
 }
 
 @inproceedings{Quinlan1992,
    address = {Singapore},
    author = {Ross J. Quinlan},
    booktitle = {5th Australian Joint Conference on Artificial Intelligence},
    pages = {343-348},
    publisher = {World Scientific},
    title = {Learning with Continuous Classes},
    year = {1992}
 }
 
 @inproceedings{Wang1997,
    author = {Y. Wang and I. H. Witten},
    booktitle = {Poster papers of the 9th European Conference on Machine Learning},
    publisher = {Springer},
    title = {Induction of model trees for predicting continuous classes},
    year = {1997}
 }

Valid options are: 

 -N
  Use unpruned tree/rules
 -U
  Use unsmoothed predictions
 -R
  Build regression tree/rule rather than a model tree/rule
 -M <minimum number of instances>
  Set minimum number of instances per leaf
  (default 4)
Version:
$Revision: 8034 $
Author:
Mark Hall
See Also:

  • Constructor Details

    • M5Rules

      public M5Rules()
      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
      Overrides:
      getTechnicalInformation in class M5Base
      Returns:
      the technical information about this class

    • 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 by which this class can be tested
      Parameters:
      args - an array of options