Package weka.classifiers.rules

Class DecisionTable

java.lang.Object
weka.classifiers.AbstractClassifier
weka.classifiers.rules.DecisionTable
All Implemented Interfaces:
Serializable, Cloneable, Classifier, AdditionalMeasureProducer, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler
public class DecisionTable extends AbstractClassifier implements OptionHandler, WeightedInstancesHandler, AdditionalMeasureProducer, TechnicalInformationHandler
Class for building and using a simple decision table majority classifier.

For more information see:

Ron Kohavi: The Power of Decision Tables. In: 8th European Conference on Machine Learning, 174-189, 1995.

BibTeX: 

 @inproceedings{Kohavi1995,
    author = {Ron Kohavi},
    booktitle = {8th European Conference on Machine Learning},
    pages = {174-189},
    publisher = {Springer},
    title = {The Power of Decision Tables},
    year = {1995}
 }

Valid options are: 

 -S <search method specification>
  Full class name of search method, followed
  by its options.
  eg: "weka.attributeSelection.BestFirst -D 1"
  (default weka.attributeSelection.BestFirst)
 
 -X <number of folds>
  Use cross validation to evaluate features.
  Use number of folds = 1 for leave one out CV.
  (Default = leave one out CV)
 
 -E <acc | rmse | mae | auc>
  Performance evaluation measure to use for selecting attributes.
  (Default = accuracy for discrete class and rmse for numeric class)
 
 -I
  Use nearest neighbour instead of global table majority.
 
 -R
  Display decision table rules.
 
 Options specific to search method weka.attributeSelection.BestFirst:
 
 -P <start set>
  Specify a starting set of attributes.
  Eg. 1,3,5-7.
 
 -D <0 = backward | 1 = forward | 2 = bi-directional>
  Direction of search. (default = 1).
 
 -N <num>
  Number of non-improving nodes to
  consider before terminating search.
 
 -S <num>
  Size of lookup cache for evaluated subsets.
  Expressed as a multiple of the number of
  attributes in the data set. (default = 1)
Version:
$Revision: 15519 $
Author:
Mark Hall (mhall@cs.waikato.ac.nz)
See Also:

  • Field Details

  • Constructor Details

    • DecisionTable

      public DecisionTable()
      Constructor for a DecisionTable

  • 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
      Returns:
      the technical information about this class

    • listOptions

      public Enumeration<Option> listOptions()
      Returns an enumeration describing the available options.
      Specified by:
      listOptions in interface OptionHandler
      Overrides:
      listOptions in class AbstractClassifier
      Returns:
      an enumeration of all the available options.

    • crossValTipText

      public String crossValTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • setCrossVal

      public void setCrossVal(int folds)
      Sets the number of folds for cross validation (1 = leave one out)
      Parameters:
      folds - the number of folds

    • getCrossVal

      public int getCrossVal()
      Gets the number of folds for cross validation
      Returns:
      the number of cross validation folds

    • useIBkTipText

      public String useIBkTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • setUseIBk

      public void setUseIBk(boolean ibk)
      Sets whether IBk should be used instead of the majority class
      Parameters:
      ibk - true if IBk is to be used

    • getUseIBk

      public boolean getUseIBk()
      Gets whether IBk is being used instead of the majority class
      Returns:
      true if IBk is being used

    • displayRulesTipText

      public String displayRulesTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • setDisplayRules

      public void setDisplayRules(boolean rules)
      Sets whether rules are to be printed
      Parameters:
      rules - true if rules are to be printed

    • getDisplayRules

      public boolean getDisplayRules()
      Gets whether rules are being printed
      Returns:
      true if rules are being printed

    • searchTipText

      public String searchTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • setSearch

      public void setSearch(ASSearch search)
      Sets the search method to use
      Parameters:
      search -

    • getSearch

      public ASSearch getSearch()
      Gets the current search method
      Returns:
      the search method used

    • evaluationMeasureTipText

      public String evaluationMeasureTipText()
      Returns the tip text for this property
      Returns:
      tip text for this property suitable for displaying in the explorer/experimenter gui

    • getEvaluationMeasure

      public SelectedTag getEvaluationMeasure()
      Gets the currently set performance evaluation measure used for selecting attributes for the decision table
      Returns:
      the performance evaluation measure

    • setEvaluationMeasure

      public void setEvaluationMeasure(SelectedTag newMethod)
      Sets the performance evaluation measure to use for selecting attributes for the decision table
      Parameters:
      newMethod - the new performance evaluation metric to use

    • setOptions

      public void setOptions(String[] options) throws Exception
      Parses the options for this object.

      Valid options are: 

       -S <search method specification>
  Full class name of search method, followed
  by its options.
  eg: "weka.attributeSelection.BestFirst -D 1"
  (default weka.attributeSelection.BestFirst)
 
       -X <number of folds>
  Use cross validation to evaluate features.
  Use number of folds = 1 for leave one out CV.
  (Default = leave one out CV)
 
       -E <acc | rmse | mae | auc>
  Performance evaluation measure to use for selecting attributes.
  (Default = accuracy for discrete class and rmse for numeric class)
 
       -I
  Use nearest neighbour instead of global table majority.
 
       -R
  Display decision table rules.
 
       Options specific to search method weka.attributeSelection.BestFirst:
 
       -P <start set>
  Specify a starting set of attributes.
  Eg. 1,3,5-7.
 
       -D <0 = backward | 1 = forward | 2 = bi-directional>
  Direction of search. (default = 1).
 
       -N <num>
  Number of non-improving nodes to
  consider before terminating search.
 
       -S <num>
  Size of lookup cache for evaluated subsets.
  Expressed as a multiple of the number of
  attributes in the data set. (default = 1)
      Specified by:
      setOptions in interface OptionHandler
      Overrides:
      setOptions in class AbstractClassifier
      Parameters:
      options - the list of options as an array of strings
      Throws:
      Exception - if an option is not supported

    • getOptions

      public String[] getOptions()
      Gets the current settings of the classifier.
      Specified by:
      getOptions in interface OptionHandler
      Overrides:
      getOptions in class AbstractClassifier
      Returns:
      an array of strings suitable for passing to setOptions

    • getCapabilities

      public Capabilities getCapabilities()
      Returns default capabilities of the classifier.
      Specified by:
      getCapabilities in interface CapabilitiesHandler
      Specified by:
      getCapabilities in interface Classifier
      Overrides:
      getCapabilities in class AbstractClassifier
      Returns:
      the capabilities of this classifier
      See Also:

    • buildClassifier

      public void buildClassifier(Instances data) throws Exception
      Generates the classifier.
      Specified by:
      buildClassifier in interface Classifier
      Parameters:
      data - set of instances serving as training data
      Throws:
      Exception - if the classifier has not been generated successfully

    • distributionForInstance

      public double[] distributionForInstance(Instance instance) throws Exception
      Calculates the class membership probabilities for the given test instance.
      Specified by:
      distributionForInstance in interface Classifier
      Overrides:
      distributionForInstance in class AbstractClassifier
      Parameters:
      instance - the instance to be classified
      Returns:
      predicted class probability distribution
      Throws:
      Exception - if distribution can't be computed

    • printFeatures

      public String printFeatures()
      Returns a string description of the features selected
      Returns:
      a string of features

    • measureNumRules

      public double measureNumRules()
      Returns the number of rules
      Returns:
      the number of rules

    • enumerateMeasures

      public Enumeration<String> enumerateMeasures()
      Returns an enumeration of the additional measure names
      Specified by:
      enumerateMeasures in interface AdditionalMeasureProducer
      Returns:
      an enumeration of the measure names

    • getMeasure

      public double getMeasure(String additionalMeasureName)
      Returns the value of the named measure
      Specified by:
      getMeasure in interface AdditionalMeasureProducer
      Parameters:
      additionalMeasureName - the name of the measure to query for its value
      Returns:
      the value of the named measure
      Throws:
      IllegalArgumentException - if the named measure is not supported

    • toString

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

    • 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[] argv)
      Main method for testing this class.
      Parameters:
      argv - the command-line options