Implementations of algorithms for data analysis based on the rough set theory (RST) and the fuzzy rough set theory (FRST). We not only provide implementations for the basic concepts of RST and FRST but also popular algorithms that derive from those theories. The methods included in the package can be divided into several categories based on their functionality: discretization, feature selection, instance selection, rule induction and classification based on nearest neighbors. RST was introduced by Zdzisław Pawlak in 1982 as a sophisticated mathematical tool to model and process imprecise or incomplete information. By using the indiscernibility relation for objects/instances, RST does not require additional parameters to analyze the data. FRST is an extension of RST. The FRST combines concepts of vagueness and indiscernibility that are expressed with fuzzy sets (as proposed by Zadeh, in 1965) and RST.
|Author||Lala Septem Riza [aut], Andrzej Janusz [aut], Dominik Ślęzak [ctb], Chris Cornelis [ctb], Francisco Herrera [ctb], Jose Manuel Benitez [ctb], Christoph Bergmeir [ctb, cre], Sebastian Stawicki [ctb]|
|Date of publication||2015-09-05 09:37:46|
|Maintainer||Christoph Bergmeir <email@example.com>|
|License||GPL (>= 2)|
A.Introduction-RoughSets: Introduction to Rough Set Theory
as.character.RuleSetRST: The 'as.character' method for RST rule sets
as.list.RuleSetRST: The 'as.list' method for RST rule sets
BC.discernibility.mat.FRST: The decision-relative discernibility matrix based on fuzzy...
BC.discernibility.mat.RST: Computation of a decision-relative discernibility matrix...
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...
BC.LU.approximation.FRST: The fuzzy lower and upper approximations based on fuzzy rough...
BC.LU.approximation.RST: Computation of lower and upper approximations of decision...
BC.positive.reg.FRST: Positive region based on fuzzy rough set
BC.positive.reg.RST: Computation of a positive region
B.Introduction-FuzzyRoughSets: Introduction to Fuzzy Rough Set Theory
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...
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...
D.local.discernibility.heuristic.RST: Supervised discretization based on the local discernibility...
FS.all.reducts.computation: A function for computing all decision reducts of a decision...
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...
FS.greedy.heuristic.superreduct.RST: The greedy heuristic method for determining superreduct based...
FS.nearOpt.fvprs.FRST: The near-optimal reduction algorithm based on fuzzy rough set...
FS.one.reduct.computation: Computing one reduct from a discernibility matrix
FS.permutation.heuristic.reduct.RST: The permutation heuristic algorithm for computation of a...
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
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...
MV.mostCommonValResConcept: The most common value or mean of an attribute restricted to a...
predict.RuleSetFRST: The predicting function for rule induction methods based on...
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.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
RoughSetData: Data set of the package
RoughSets-package: 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...
SF.read.DecisionTable: Reading tabular data from files.
sub-.RuleSetRST: The '[.' method for '"RuleSetRST"' objects
summary.IndiscernibilityRelation: The summary function for an indiscernibility relation
summary.LowerUpperApproximation: The summary function of lower and upper approximations based...
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