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java.lang.Objectweka.classifiers.rules.Rule
weka.classifiers.rules.FURIA.RipperRule
public class FURIA.RipperRule
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.
Field Summary | |
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FastVector |
m_Antds
The vector of antecedents of this rule |
Constructor Summary | |
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FURIA.RipperRule()
Constructor |
Method Summary | |
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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()
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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 |
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equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Field Detail |
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public FastVector m_Antds
Constructor Detail |
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public FURIA.RipperRule()
Method Detail |
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public void setConsequent(double cl)
cl
- the internal representation of the class label to be predictedpublic double getConsequent()
getConsequent
in class Rule
public java.lang.Object copy()
copy
in interface Copyable
copy
in class Rule
public double coverageDegree(Instance datum)
datum
- the instance in question
public boolean covers(Instance datum)
covers
in class Rule
datum
- the instance in question
public boolean hasAntds()
hasAntds
in class Rule
public double size()
size
in class Rule
public void grow(Instances data) throws java.lang.Exception
grow
in class Rule
data
- the growing data used to build the rule
java.lang.Exception
- if the consequent is not set yetpublic void prune(Instances pruneData, boolean useWhole)
pruneData
- the pruning data used to prune the ruleuseWhole
- flag to indicate whether use the error rate of
the whole pruning data instead of the data coveredpublic java.lang.String toString(Attribute classAttr)
classAttr
- the class attribute in the data
public void fuzzify(Instances data, boolean allWeightsAreOne)
data
- training dataallWeightsAreOne
- flag whether all instances have weight 1. If this is the case branch-and-bound is possible for speed-up.public void calculateConfidences(Instances data)
data
- The training datapublic double getConfidence()
public java.lang.String getRevision()
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