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B
buildClassifier(Instances)
- Method in class weka.classifiers.rules.
FURIA
Builds the FURIA rule-based model
C
calculateConfidences(Instances)
- Method in class weka.classifiers.rules.
FURIA.RipperRule
Calculation of the rule weights / confidences for all beginning rule stumps.
checkErrorRateTipText()
- Method in class weka.classifiers.rules.
FURIA
Returns the tip text for this property
copy()
- Method in class weka.classifiers.rules.
FURIA.NumericAntd
Implements Copyable
copy()
- Method in class weka.classifiers.rules.
FURIA.RipperRule
Get a shallow copy of this rule
coverageDegree(Instance)
- Method in class weka.classifiers.rules.
FURIA.RipperRule
The degree of coverage instance covered by this rule
covers(Instance)
- Method in class weka.classifiers.rules.
FURIA.NumericAntd
The degree of coverage for the instance given that antecedent
covers(Instance)
- Method in class weka.classifiers.rules.
FURIA.RipperRule
Whether the instance covered by this rule
D
debugTipText()
- Method in class weka.classifiers.rules.
FURIA
Returns the tip text for this property
distributionForInstance(Instance)
- Method in class weka.classifiers.rules.
FURIA
Classify the test instance with the rule learner and provide the class distributions
E
enumerateMeasures()
- Method in class weka.classifiers.rules.
FURIA
Returns an enumeration of the additional measure names
F
foldsTipText()
- Method in class weka.classifiers.rules.
FURIA
Returns the tip text for this property
FURIA
- Class in
weka.classifiers.rules
FURIA: Fuzzy Unordered Rule Induction Algorithm
Details please see:
Jens Christian Huehn, Eyke Huellermeier (2009).
FURIA()
- Constructor for class weka.classifiers.rules.
FURIA
FURIA.NumericAntd
- Class in
weka.classifiers.rules
The antecedent with numeric attribute
FURIA.NumericAntd(Attribute)
- Constructor for class weka.classifiers.rules.
FURIA.NumericAntd
Constructor
FURIA.RipperRule
- Class in
weka.classifiers.rules
This class implements a single rule that predicts specified class.
FURIA.RipperRule()
- Constructor for class weka.classifiers.rules.
FURIA.RipperRule
Constructor
fuzzify(Instances, boolean)
- Method in class weka.classifiers.rules.
FURIA.RipperRule
The fuzzification procedure
fuzzyYet
- Variable in class weka.classifiers.rules.
FURIA.NumericAntd
A flag determining whether this antecedent was successfully fuzzified yet
G
getCapabilities()
- Method in class weka.classifiers.rules.
FURIA
Returns default capabilities of the classifier.
getCheckErrorRate()
- Method in class weka.classifiers.rules.
FURIA
Gets whether to check for error rate is in stopping criterion
getConfidence()
- Method in class weka.classifiers.rules.
FURIA.RipperRule
Get the rule confidence.
getConsequent()
- Method in class weka.classifiers.rules.
FURIA.RipperRule
Gets the internal representation of the class label to be predicted
getDebug()
- Method in class weka.classifiers.rules.
FURIA
Gets whether debug information is output to the console
getFolds()
- Method in class weka.classifiers.rules.
FURIA
Gets the number of folds
getMeasure(String)
- Method in class weka.classifiers.rules.
FURIA
Returns the value of the named measure
getMinNo()
- Method in class weka.classifiers.rules.
FURIA
Gets the minimum total weight of the instances in a rule
getOptimizations()
- Method in class weka.classifiers.rules.
FURIA
Gets the the number of optimization runs
getOptions()
- Method in class weka.classifiers.rules.
FURIA
Gets the current settings of the Classifier.
getRevision()
- Method in class weka.classifiers.rules.
FURIA
getRevision()
- Method in class weka.classifiers.rules.
FURIA.RipperRule
getRuleset()
- Method in class weka.classifiers.rules.
FURIA
Get the ruleset generated by FURIA
getRuleStats(int)
- Method in class weka.classifiers.rules.
FURIA
Get the statistics of the ruleset in the given position
getSeed()
- Method in class weka.classifiers.rules.
FURIA
Gets the current seed value to use in randomizing the data
getSplitPoint()
- Method in class weka.classifiers.rules.
FURIA.NumericAntd
Get split point of this numeric antecedent
getTechnicalInformation()
- Method in class weka.classifiers.rules.
FURIA
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.
getTNorm()
- Method in class weka.classifiers.rules.
FURIA
Gets the TNorm used.
getUncovAction()
- Method in class weka.classifiers.rules.
FURIA
Gets the action that is performed for uncovered instances.
globalInfo()
- Method in class weka.classifiers.rules.
FURIA
Returns a string describing classifier
grow(Instances)
- Method in class weka.classifiers.rules.
FURIA.RipperRule
Build one rule using the growing data
H
hasAntds()
- Method in class weka.classifiers.rules.
FURIA.RipperRule
Whether this rule has antecedents, i.e.
L
listOptions()
- Method in class weka.classifiers.rules.
FURIA
Returns an enumeration describing the available options Valid options are:
M
m_Antds
- Variable in class weka.classifiers.rules.
FURIA.RipperRule
The vector of antecedents of this rule
main(String[])
- Static method in class weka.classifiers.rules.
FURIA
Main method.
minNoTipText()
- Method in class weka.classifiers.rules.
FURIA
Returns the tip text for this property
O
optimizationsTipText()
- Method in class weka.classifiers.rules.
FURIA
Returns the tip text for this property
P
prune(Instances, boolean)
- Method in class weka.classifiers.rules.
FURIA.RipperRule
Prune all the possible final sequences of the rule using the pruning data.
S
seedTipText()
- Method in class weka.classifiers.rules.
FURIA
Returns the tip text for this property
setCheckErrorRate(boolean)
- Method in class weka.classifiers.rules.
FURIA
Sets whether to check for error rate is in stopping criterion
setConsequent(double)
- Method in class weka.classifiers.rules.
FURIA.RipperRule
Sets the internal representation of the class label to be predicted
setDebug(boolean)
- Method in class weka.classifiers.rules.
FURIA
Sets whether debug information is output to the console
setFolds(int)
- Method in class weka.classifiers.rules.
FURIA
Sets the number of folds to use
setMinNo(double)
- Method in class weka.classifiers.rules.
FURIA
Sets the minimum total weight of the instances in a rule
setOptimizations(int)
- Method in class weka.classifiers.rules.
FURIA
Sets the number of optimization runs
setOptions(String[])
- Method in class weka.classifiers.rules.
FURIA
Parses a given list of options.
setSeed(long)
- Method in class weka.classifiers.rules.
FURIA
Sets the seed value to use in randomizing the data
setTNorm(SelectedTag)
- Method in class weka.classifiers.rules.
FURIA
Sets the TNorm used.
setUncovAction(SelectedTag)
- Method in class weka.classifiers.rules.
FURIA
Sets the action that is performed for uncovered instances.
size()
- Method in class weka.classifiers.rules.
FURIA.RipperRule
the number of antecedents of the rule
splitData(Instances, double, double)
- Method in class weka.classifiers.rules.
FURIA.NumericAntd
Implements the splitData function.
splitPoint
- Variable in class weka.classifiers.rules.
FURIA.NumericAntd
The split point for this numeric antecedent
supportBound
- Variable in class weka.classifiers.rules.
FURIA.NumericAntd
The edge point for the fuzzy set of this numeric antecedent
T
TNormTipText()
- Method in class weka.classifiers.rules.
FURIA
Returns the tip text for this property
toString()
- Method in class weka.classifiers.rules.
FURIA.NumericAntd
Prints this antecedent
toString(Attribute)
- Method in class weka.classifiers.rules.
FURIA.RipperRule
Prints this rule
toString()
- Method in class weka.classifiers.rules.
FURIA
Prints the all the rules of the rule learner.
U
uncovActionTipText()
- Method in class weka.classifiers.rules.
FURIA
Returns the tip text for this property
W
weka.classifiers.rules
- package weka.classifiers.rules
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