content: /*
   *    This program is free software; you can redistribute it and/or modify
   *    it under the terms of the GNU General Public License as published by
   *    the Free Software Foundation; either version 2 of the License, or
   *    (at your option) any later version.
   *
   *    This program is distributed in the hope that it will be useful,
   *    but WITHOUT ANY WARRANTY; without even the implied warranty of
   *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
   *    GNU General Public License for more details.
   *
   *    You should have received a copy of the GNU General Public License
   *    along with this program; if not, write to the Free Software
   *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
   */
  
  /*
   *    M5Rules.java
   *    Copyright (C) 2001 University of Waikato, Hamilton, New Zealand
   */
  
  package weka.classifiers.rules;
  
  import weka.classifiers.trees.m5.M5Base;
  import weka.core.RevisionUtils;
  import weka.core.TechnicalInformation;
  import weka.core.TechnicalInformationHandler;
  import weka.core.TechnicalInformation.Field;
  import weka.core.TechnicalInformation.Type;
  
  /**
   <!-- globalinfo-start -->
   * 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.<br/>
   * <br/>
   * For more information see:<br/>
   * <br/>
   * Geoffrey Holmes, Mark Hall, Eibe Frank: Generating Rule Sets from Model Trees. In: Twelfth Australian Joint Conference on Artificial Intelligence, 1-12, 1999.<br/>
   * <br/>
   * Ross J. Quinlan: Learning with Continuous Classes. In: 5th Australian Joint Conference on Artificial Intelligence, Singapore, 343-348, 1992.<br/>
   * <br/>
   * 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.
   * <p/>
   <!-- globalinfo-end -->
   *
   <!-- technical-bibtex-start -->
   * BibTeX:
   * <pre>
   * &#64;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}
   * }
   * 
   * &#64;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}
   * }
   * 
   * &#64;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}
   * }
   * </pre>
   * <p/>
   <!-- technical-bibtex-end -->
   *
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -N
   *  Use unpruned tree/rules</pre>
   * 
   * <pre> -U
   *  Use unsmoothed predictions</pre>
   * 
   * <pre> -R
   *  Build regression tree/rule rather than a model tree/rule</pre>
   * 
   * <pre> -M &lt;minimum number of instances&gt;
   *  Set minimum number of instances per leaf
   *  (default 4)</pre>
   * 
   <!-- options-end -->
   *
   * @author <a href="mailto:mhall@cs.waikato.ac.nz">Mark Hall</a>
   * @version $Revision: 1.11 $
   */
  public class M5Rules 
    extends M5Base
    implements TechnicalInformationHandler {
      
    /** for serialization */
    static final long serialVersionUID = -1746114858746563180L;
    
    /**
     * Returns a string describing classifier
     * @return a description suitable for
     * displaying in the explorer/experimenter gui
     */
    public String globalInfo() {
  
      return "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.\n\n"
        + "For more information see:\n\n"
        + getTechnicalInformation().toString();
    }
  
    /**
     * Constructor
     */
    public M5Rules() {
      super();
      setGenerateRules(true);
    }
  
    /**
     * 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.
     * 
     * @return the technical information about this class
     */
    public TechnicalInformation getTechnicalInformation() {
      TechnicalInformation 	result;
      
      result = new TechnicalInformation(Type.INPROCEEDINGS);
      result.setValue(Field.AUTHOR, "Geoffrey Holmes and Mark Hall and Eibe Frank");
      result.setValue(Field.TITLE, "Generating Rule Sets from Model Trees");
      result.setValue(Field.BOOKTITLE, "Twelfth Australian Joint Conference on Artificial Intelligence");
      result.setValue(Field.YEAR, "1999");
      result.setValue(Field.PAGES, "1-12");
      result.setValue(Field.PUBLISHER, "Springer");
      
      result.add(super.getTechnicalInformation());
      
      return result;
    }
    
    /**
     * Returns the revision string.
     * 
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 1.11 $");
    }
  
    /**
     * Main method by which this class can be tested
     * 
     * @param args an array of options
     */
    public static void main(String[] args) {
      runClassifier(new M5Rules(), args);
    } 
  }
  