RoughSets-package |
Getting started with the RoughSets package |
as.character.RuleSetRST |
The 'as.character' method for RST rule sets |
as.list.RuleSetRST |
The 'as.list' method for RST rule sets |
BC.boundary.reg.RST |
Computation of a boundary region |
BC.discernibility.mat.FRST |
The decision-relative discernibility matrix based on fuzzy rough set theory |
BC.discernibility.mat.RST |
Computation of a decision-relative discernibility matrix based on the rough set theory |
BC.IND.relation.FRST |
The indiscernibility relation based on fuzzy rough set theory |
BC.IND.relation.RST |
Computation of indiscernibility classes based on the rough set theory |
BC.LU.approximation.FRST |
The fuzzy lower and upper approximations based on fuzzy rough set theory |
BC.LU.approximation.RST |
Computation of lower and upper approximations of decision classes |
BC.negative.reg.RST |
Computation of a negative region |
BC.positive.reg.FRST |
Positive region based on fuzzy rough set |
BC.positive.reg.RST |
Computation of a positive region |
C.FRNN.FRST |
The fuzzy-rough nearest neighbor algorithm |
C.FRNN.O.FRST |
The fuzzy-rough ownership nearest neighbor algorithm |
C.POSNN.FRST |
The positive region based fuzzy-rough nearest neighbor algorithm |
D.discretization.RST |
The wrapper function for discretization methods |
D.discretize.equal.intervals.RST |
Unsupervised discretization into intervals of equal length. |
D.discretize.quantiles.RST |
The quantile-based discretization |
D.global.discernibility.heuristic.RST |
Supervised discretization based on the maximum discernibility heuristic |
D.local.discernibility.heuristic.RST |
Supervised discretization based on the local discernibility heuristic |
Extract.RuleSetRST |
The '[.' method for '"RuleSetRST"' objects |
FS.all.reducts.computation |
A function for computing all decision reducts of a decision system |
FS.DAAR.heuristic.RST |
The DAAR heuristic for computation of decision reducts |
FS.feature.subset.computation |
The superreduct computation based on RST and FRST |
FS.greedy.heuristic.reduct.RST |
The greedy heuristic algorithm for computing decision reducts and approximate decision reducts |
FS.greedy.heuristic.superreduct.RST |
The greedy heuristic method for determining superreduct based on RST |
FS.nearOpt.fvprs.FRST |
The near-optimal reduction algorithm based on fuzzy rough set theory |
FS.one.reduct.computation |
Computing one reduct from a discernibility matrix |
FS.permutation.heuristic.reduct.RST |
The permutation heuristic algorithm for computation of a decision reduct |
FS.quickreduct.FRST |
The fuzzy QuickReduct algorithm based on FRST |
FS.quickreduct.RST |
QuickReduct algorithm based on RST |
FS.reduct.computation |
The reduct computation methods based on RST and FRST |
Introduction-FuzzyRoughSets |
Getting started with the RoughSets package |
Introduction-RoughSets |
Getting started with the RoughSets package |
IS.FRIS.FRST |
The fuzzy rough instance selection algorithm |
IS.FRPS.FRST |
The fuzzy rough prototype selection method |
MV.conceptClosestFit |
Concept Closest Fit |
MV.deletionCases |
Missing value completion by deleting instances |
MV.globalClosestFit |
Global Closest Fit |
MV.missingValueCompletion |
Wrapper function of missing value completion |
MV.mostCommonVal |
Replacing missing attribute values by the attribute mean or common values |
MV.mostCommonValResConcept |
The most common value or mean of an attribute restricted to a concept |
predict.FRST |
The predicting function for rule induction methods based on FRST |
predict.RST |
Prediction of decision classes using rule-based classifiers. |
predict.RuleSetFRST |
The predicting function for rule induction methods based on FRST |
predict.RuleSetRST |
Prediction of decision classes using rule-based classifiers. |
print.FeatureSubset |
The print method of FeatureSubset objects |
print.RuleSetRST |
The print function for RST rule sets |
RI.AQRules.RST |
Rule induction using the AQ algorithm |
RI.CN2Rules.RST |
Rule induction using a version of CN2 algorithm |
RI.confidence |
Quality indicators of RST decision rules |
RI.GFRS.FRST |
Generalized fuzzy rough set rule induction based on FRST |
RI.hybridFS.FRST |
Hybrid fuzzy-rough rule and induction and feature selection |
RI.indiscernibilityBasedRules.RST |
Rule induction from indiscernibility classes. |
RI.laplace |
Quality indicators of RST decision rules |
RI.LEM2Rules.RST |
Rule induction using the LEM2 algorithm |
RI.lift |
Quality indicators of RST decision rules |
RI.support |
Quality indicators of RST decision rules |
RoughSetData |
Data set of the package |
RoughSets |
Getting started with the RoughSets package |
SF.applyDecTable |
Apply for obtaining a new decision table |
SF.asDecisionTable |
Converting a data.frame into a 'DecisionTable' object |
SF.asFeatureSubset |
Converting custom attribute name sets into a FeatureSubset object |
SF.read.DecisionTable |
Reading tabular data from files. |
summary.IndiscernibilityRelation |
The summary function for an indiscernibility relation |
summary.LowerUpperApproximation |
The summary function of lower and upper approximations based on RST and FRST |
summary.PositiveRegion |
The summary function of positive region based on RST and FRST |
summary.RuleSetFRST |
The summary function of rules based on FRST |
summary.RuleSetRST |
The summary function of rules based on RST |
X.entropy |
The entropy measure |
X.gini |
The gini-index measure |
X.laplace |
Rule voting by the Laplace estimate |
X.nOfConflicts |
The discernibility measure |
X.rulesCounting |
Rule voting by counting matching rules |
X.ruleStrength |
Rule voting by strength of the rule |
[.RuleSetRST |
The '[.' method for '"RuleSetRST"' objects |