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java.lang.Objectweka.classifiers.Classifier
weka.classifiers.trees.m5.M5Base
weka.classifiers.rules.M5Rules
public class M5Rules
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.
@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)
Constructor Summary | |
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M5Rules()
Constructor |
Method Summary | |
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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. |
java.lang.String |
globalInfo()
Returns a string describing classifier |
static void |
main(java.lang.String[] args)
Main method by which this class can be tested |
Methods inherited from class weka.classifiers.trees.m5.M5Base |
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buildClassifier, buildRegressionTreeTipText, classifyInstance, enumerateMeasures, generateRulesTipText, getBuildRegressionTree, getCapabilities, getM5RootNode, getMeasure, getMinNumInstances, getOptions, getUnpruned, getUseUnsmoothed, listOptions, measureNumRules, minNumInstancesTipText, setBuildRegressionTree, setMinNumInstances, setOptions, setUnpruned, setUseUnsmoothed, toString, unprunedTipText, useUnsmoothedTipText |
Methods inherited from class weka.classifiers.Classifier |
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debugTipText, distributionForInstance, forName, getDebug, makeCopies, makeCopy, setDebug |
Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
Constructor Detail |
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public M5Rules()
Method Detail |
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public java.lang.String globalInfo()
globalInfo
in class M5Base
public TechnicalInformation getTechnicalInformation()
getTechnicalInformation
in interface TechnicalInformationHandler
getTechnicalInformation
in class M5Base
public static void main(java.lang.String[] args)
args
- an array of options
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