weka.classifiers.rules

## Class DecisionTable

- java.lang.Object
- - weka.classifiers.AbstractClassifier
  - - weka.classifiers.rules.DecisionTable

- All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, Classifier, AdditionalMeasureProducer, CapabilitiesHandler, CapabilitiesIgnorer, 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: 10153 $
  Author:
    Mark Hall (mhall@cs.waikato.ac.nz)
  See Also:
    Serialized Form

- - ### Field Summary

    Fields
    Modifier and Type | Field and Description
    --- | ---
    `static int` | `EVAL_ACCURACY`
    `static int` | `EVAL_AUC`
    `static int` | `EVAL_DEFAULT` default is accuracy for discrete class and RMSE for numeric class
    `static int` | `EVAL_MAE`
    `static int` | `EVAL_RMSE`
    `static Tag[]` | `TAGS_EVALUATION`

  - ### Constructor Summary

    Constructors
    Constructor and Description
    `DecisionTable()` Constructor for a DecisionTable

  - ### Method Summary

    Methods
    Modifier and Type | Method and Description
    --- | ---
    `void` | `buildClassifier(Instances data)` Generates the classifier.
    `java.lang.String` | `crossValTipText()` Returns the tip text for this property
    `java.lang.String` | `displayRulesTipText()` Returns the tip text for this property
    `double[]` | `distributionForInstance(Instance instance)` Calculates the class membership probabilities for the given test instance.
    `java.util.Enumeration<java.lang.String>` | `enumerateMeasures()` Returns an enumeration of the additional measure names
    `java.lang.String` | `evaluationMeasureTipText()` Returns the tip text for this property
    `Capabilities` | `getCapabilities()` Returns default capabilities of the classifier.
    `int` | `getCrossVal()` Gets the number of folds for cross validation
    `boolean` | `getDisplayRules()` Gets whether rules are being printed
    `SelectedTag` | `getEvaluationMeasure()` Gets the currently set performance evaluation measure used for selecting attributes for the decision table
    `double` | `getMeasure(java.lang.String additionalMeasureName)` Returns the value of the named measure
    `java.lang.String[]` | `getOptions()` Gets the current settings of the classifier.
    `java.lang.String` | `getRevision()` Returns the revision string.
    `ASSearch` | `getSearch()` Gets the current search method
    `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.
    `boolean` | `getUseIBk()` Gets whether IBk is being used instead of the majority class
    `java.lang.String` | `globalInfo()` Returns a string describing classifier
    `java.util.Enumeration<Option>` | `listOptions()` Returns an enumeration describing the available options.
    `static void` | `main(java.lang.String[] argv)` Main method for testing this class.
    `double` | `measureNumRules()` Returns the number of rules
    `java.lang.String` | `printFeatures()` Returns a string description of the features selected
    `java.lang.String` | `searchTipText()` Returns the tip text for this property
    `void` | `setCrossVal(int folds)` Sets the number of folds for cross validation (1 = leave one out)
    `void` | `setDisplayRules(boolean rules)` Sets whether rules are to be printed
    `void` | `setEvaluationMeasure(SelectedTag newMethod)` Sets the performance evaluation measure to use for selecting attributes for the decision table
    `void` | `setOptions(java.lang.String[] options)` Parses the options for this object.
    `void` | `setSearch(ASSearch search)` Sets the search method to use
    `void` | `setUseIBk(boolean ibk)` Sets whether IBk should be used instead of the majority class
    `java.lang.String` | `toString()` Returns a description of the classifier.
    `java.lang.String` | `useIBkTipText()` Returns the tip text for this property

    - ### Methods inherited from class weka.classifiers.AbstractClassifier

      `classifyInstance, debugTipText, doNotCheckCapabilitiesTipText, forName, getDebug, getDoNotCheckCapabilities, makeCopies, makeCopy, runClassifier, setDebug, setDoNotCheckCapabilities`

    - ### Methods inherited from class java.lang.Object

      `equals, getClass, hashCode, notify, notifyAll, wait, wait, wait`

- - ### Field Detail

    - #### EVAL_DEFAULT

          public static final int EVAL_DEFAULT

      default is accuracy for discrete class and RMSE for numeric class
      See Also:
        Constant Field Values

    - #### EVAL_ACCURACY

          public static final int EVAL_ACCURACY

      See Also:
        Constant Field Values

    - #### EVAL_RMSE

          public static final int EVAL_RMSE

      See Also:
        Constant Field Values

    - #### EVAL_MAE

          public static final int EVAL_MAE

      See Also:
        Constant Field Values

    - #### EVAL_AUC

          public static final int EVAL_AUC

      See Also:
        Constant Field Values

    - #### TAGS_EVALUATION

          public static final Tag[] TAGS_EVALUATION

  - ### Constructor Detail

    - #### DecisionTable

          public DecisionTable()

      Constructor for a DecisionTable

  - ### Method Detail

    - #### globalInfo

          public java.lang.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 java.util.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 java.lang.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 java.lang.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 java.lang.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 java.lang.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 java.lang.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(java.lang.String[] options)
                          throws java.lang.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:
        `java.lang.Exception` - if an option is not supported

    - #### getOptions

          public java.lang.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 `Classifier`
      Specified by:
        `getCapabilities` in interface `CapabilitiesHandler`
      Overrides:
        `getCapabilities` in class `AbstractClassifier`
      Returns:
        the capabilities of this classifier
      See Also:
        `Capabilities`

    - #### buildClassifier

          public void buildClassifier(Instances data)
                               throws java.lang.Exception

      Generates the classifier.
      Specified by:
        `buildClassifier` in interface `Classifier`
      Parameters:
        `data` - set of instances serving as training data
      Throws:
        `java.lang.Exception` - if the classifier has not been generated successfully

    - #### distributionForInstance

          public double[] distributionForInstance(Instance instance)
                                           throws java.lang.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:
        `java.lang.Exception` - if distribution can't be computed

    - #### printFeatures

          public java.lang.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 java.util.Enumeration<java.lang.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(java.lang.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:
        `java.lang.IllegalArgumentException` - if the named measure is not supported

    - #### toString

          public java.lang.String toString()

      Returns a description of the classifier.
      Overrides:
        `toString` in class `java.lang.Object`
      Returns:
        a description of the classifier as a string.

    - #### getRevision

          public java.lang.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(java.lang.String[] argv)

      Main method for testing this class.
      Parameters:
        `argv` - the command-line options