weka.classifiers.rules
Class FURIA.RipperRule

java.lang.Object
  weka.classifiers.rules.Rule
      weka.classifiers.rules.FURIA.RipperRule

All Implemented Interfaces:

java.io.Serializable, Copyable, RevisionHandler, WeightedInstancesHandler

Enclosing class:

FURIA

public class FURIA.RipperRule
extends Rule

This class implements a single rule that predicts specified class. A rule consists of antecedents "AND"ed together and the consequent (class value) for the classification. In this class, the Information Gain (p*[log(p/t) - log(P/T)]) is used to select an antecedent and Reduced Error Prunning (REP) with the metric of accuracy rate p/(p+n) or (TP+TN)/(P+N) is used to prune the rule.

See Also:

Serialized Form

Field Summary

FastVector

m_Antds The vector of antecedents of this rule

Constructor Summary

FURIA.RipperRule()
Constructor

Method Summary

void	calculateConfidences(Instances data) Calculation of the rule weights / confidences for all beginning rule stumps.
java.lang.Object	copy() Get a shallow copy of this rule
double	coverageDegree(Instance datum) The degree of coverage instance covered by this rule
boolean	covers(Instance datum) Whether the instance covered by this rule
void	fuzzify(Instances data, boolean allWeightsAreOne) The fuzzification procedure
double	getConfidence() Get the rule confidence.
double	getConsequent() Gets the internal representation of the class label to be predicted
java.lang.String	getRevision() Returns the revision string.
void	grow(Instances data) Build one rule using the growing data
boolean	hasAntds() Whether this rule has antecedents, i.e.
void	prune(Instances pruneData, boolean useWhole) Prune all the possible final sequences of the rule using the pruning data.
void	setConsequent(double cl) Sets the internal representation of the class label to be predicted
double	size() the number of antecedents of the rule
java.lang.String	toString(Attribute classAttr) Prints this rule

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

m_Antds

public FastVector m_Antds

The vector of antecedents of this rule

Constructor Detail

FURIA.RipperRule

public FURIA.RipperRule()

Constructor

Method Detail

setConsequent

public void setConsequent(double cl)

Sets the internal representation of the class label to be predicted

Parameters:

cl - the internal representation of the class label to be predicted

getConsequent

public double getConsequent()

Gets the internal representation of the class label to be predicted

Specified by:

getConsequent in class Rule

Returns:

the internal representation of the class label to be predicted

copy

public java.lang.Object copy()

Get a shallow copy of this rule

Specified by:

copy in interface Copyable

Overrides:

copy in class Rule

Returns:

the copy

coverageDegree

public double coverageDegree(Instance datum)

The degree of coverage instance covered by this rule

Parameters:

datum - the instance in question

Returns:

the degree to which the instance is covered by this rule

covers

public boolean covers(Instance datum)

Whether the instance covered by this rule

Specified by:

covers in class Rule

Parameters:

datum - the instance in question

Returns:

the boolean value indicating whether the instance is covered by this rule

hasAntds

public boolean hasAntds()

Whether this rule has antecedents, i.e. whether it is a default rule

Specified by:

hasAntds in class Rule

Returns:

the boolean value indicating whether the rule has antecedents

size

public double size()

the number of antecedents of the rule

Specified by:

size in class Rule

Returns:

the size of this rule

grow

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

Build one rule using the growing data

Specified by:

grow in class Rule

Parameters:

data - the growing data used to build the rule

Throws:

java.lang.Exception - if the consequent is not set yet

prune

public void prune(Instances pruneData,
                  boolean useWhole)

Prune all the possible final sequences of the rule using the pruning data. The measure used to prune the rule is based on flag given.

Parameters:

pruneData - the pruning data used to prune the rule

useWhole - flag to indicate whether use the error rate of the whole pruning data instead of the data covered

toString

public java.lang.String toString(Attribute classAttr)

Prints this rule

Parameters:

classAttr - the class attribute in the data

Returns:

a textual description of this rule

fuzzify

public void fuzzify(Instances data,
                    boolean allWeightsAreOne)

The fuzzification procedure

Parameters:

data - training data

allWeightsAreOne - flag whether all instances have weight 1. If this is the case branch-and-bound is possible for speed-up.

calculateConfidences

public void calculateConfidences(Instances data)

Calculation of the rule weights / confidences for all beginning rule stumps.

Parameters:

data - The training data

getConfidence

public double getConfidence()

Get the rule confidence.

Returns:

rule confidence / weight

getRevision

public java.lang.String getRevision()

Description copied from interface: RevisionHandler

Returns the revision string.

Returns:

the revision