Package weka.classifiers.rules

Class Ridor

  • All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, AdditionalMeasureProducer, CapabilitiesHandler, OptionHandler, RevisionHandler, WeightedInstancesHandler
    public class Ridor
extends Classifier
implements AdditionalMeasureProducer, WeightedInstancesHandler
    An implementation of a RIpple-DOwn Rule learner.

    It generates a default rule first and then the exceptions for the default rule with the least (weighted) error rate. Then it generates the "best" exceptions for each exception and iterates until pure. Thus it performs a tree-like expansion of exceptions.The exceptions are a set of rules that predict classes other than the default. IREP is used to generate the exceptions.

    For more information about Ripple-Down Rules, see:

    Brian R. Gaines, Paul Compton (1995). Induction of Ripple-Down Rules Applied to Modeling Large Databases. J. Intell. Inf. Syst.. 5(3):211-228.

    There are five inner classes defined in this class.
    The first is Ridor_node, which implements one node in the Ridor tree. It's basically composed of a default class and a set of exception rules to the default class.
    The second inner class is RidorRule, which implements a single exception rule using REP.
    The last three inner classes are only used in RidorRule. They are Antd, NumericAntd and NominalAntd, which all implement a single antecedent in the RidorRule.
    The Antd class is an abstract class, which has two subclasses, NumericAntd and NominalAntd, to implement the corresponding abstract functions. These two subclasses implement the functions related to a antecedent with a nominal attribute and a numeric attribute respectively.

    Valid options are: 

     -F <number of folds>
  Set number of folds for IREP
  One fold is used as pruning set.
  (default 3)
     -S <number of shuffles>
  Set number of shuffles to randomize
  the data in order to get better rule.
  (default 10)
     -A
  Set flag of whether use the error rate 
  of all the data to select the default class
  in each step. If not set, the learner will only use the error rate in the pruning data
     -M
   Set flag of whether use the majority class as
  the default class in each step instead of 
  choosing default class based on the error rate
  (if the flag is not set)
     -N <min. weights>
  Set the minimal weights of instances
  within a split.
  (default 2.0)
    Version:
    $Revision: 5529 $
    Author:
    Xin XU (xx5@cs.waikato.ac.nz)
    See Also:
    Serialized Form

    • Constructor Detail

      • Ridor

        public Ridor()

    • Method Detail

      • globalInfo

        public java.lang.String globalInfo()
        Returns a string describing classifier
        Returns:
        a description suitable for displaying in the explorer/experimenter gui

      • buildClassifier

        public void buildClassifier​(Instances instances)
                     throws java.lang.Exception
        Builds a ripple-down manner rule learner.
        Specified by:
        buildClassifier in class Classifier
        Parameters:
        instances - the training data
        Throws:
        java.lang.Exception - if classifier can't be built successfully

      • classifyInstance

        public double classifyInstance​(Instance datum)
        Classify the test instance with the rule learner
        Overrides:
        classifyInstance in class Classifier
        Parameters:
        datum - the instance to be classified
        Returns:
        the classification

      • listOptions

        public java.util.Enumeration listOptions()
        Returns an enumeration describing the available options Valid options are:

        -F number
        Set number of folds for reduced error pruning. One fold is used as the pruning set. (Default: 3)

        -S number
        Set number of shuffles for randomization. (Default: 10)

        -A
        Set flag of whether use the error rate of all the data to select the default class in each step. If not set, the learner will only use the error rate in the pruning data

        -M
        Set flag of whether use the majority class as the default class in each step instead of choosing default class based on the error rate (if the flag is not set)

        -N number
        Set the minimal weights of instances within a split. (Default: 2)

        Specified by:
        listOptions in interface OptionHandler
        Overrides:
        listOptions in class Classifier
        Returns:
        an enumeration of all the available options

      • setOptions

        public void setOptions​(java.lang.String[] options)
                throws java.lang.Exception
        Parses a given list of options.

        Valid options are: 

         -F <number of folds>
  Set number of folds for IREP
  One fold is used as pruning set.
  (default 3)
         -S <number of shuffles>
  Set number of shuffles to randomize
  the data in order to get better rule.
  (default 10)
         -A
  Set flag of whether use the error rate 
  of all the data to select the default class
  in each step. If not set, the learner will only use the error rate in the pruning data
         -M
   Set flag of whether use the majority class as
  the default class in each step instead of 
  choosing default class based on the error rate
  (if the flag is not set)
         -N <min. weights>
  Set the minimal weights of instances
  within a split.
  (default 2.0)
        Specified by:
        setOptions in interface OptionHandler
        Overrides:
        setOptions in class Classifier
        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 Classifier
        Returns:
        an array of strings suitable for passing to setOptions

      • foldsTipText

        public java.lang.String foldsTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

      • setFolds

        public void setFolds​(int fold)

      • getFolds

        public int getFolds()

      • shuffleTipText

        public java.lang.String shuffleTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

      • setShuffle

        public void setShuffle​(int sh)

      • getShuffle

        public int getShuffle()

      • seedTipText

        public java.lang.String seedTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

      • setSeed

        public void setSeed​(int s)

      • getSeed

        public int getSeed()

      • wholeDataErrTipText

        public java.lang.String wholeDataErrTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

      • setWholeDataErr

        public void setWholeDataErr​(boolean a)

      • getWholeDataErr

        public boolean getWholeDataErr()

      • majorityClassTipText

        public java.lang.String majorityClassTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

      • setMajorityClass

        public void setMajorityClass​(boolean m)

      • getMajorityClass

        public boolean getMajorityClass()

      • minNoTipText

        public java.lang.String minNoTipText()
        Returns the tip text for this property
        Returns:
        tip text for this property suitable for displaying in the explorer/experimenter gui

      • setMinNo

        public void setMinNo​(double m)

      • getMinNo

        public double getMinNo()

      • enumerateMeasures

        public java.util.Enumeration 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()
        Prints the all the rules of the rule learner.
        Overrides:
        toString in class java.lang.Object
        Returns:
        a textual description of the classifier

      • main

        public static void main​(java.lang.String[] args)
        Main method.
        Parameters:
        args - the options for the classifier