B C D E F G H L M O P S T U W

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
 

B C D E F G H L M O P S T U W