RoughSets v1.3-7

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Data Analysis Using Rough Set and Fuzzy Rough Set Theories

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 Zdzis<c5><82>aw 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.

Functions in RoughSets

Name Description
BC.discernibility.mat.RST Computation of a decision-relative discernibility matrix based on the rough set theory
A.Introduction-RoughSets Introduction to Rough Set Theory
BC.discernibility.mat.FRST The decision-relative discernibility matrix based on fuzzy rough set theory
B.Introduction-FuzzyRoughSets Introduction to Fuzzy Rough Set Theory
BC.IND.relation.RST Computation of indiscernibility classes based on the rough set theory
BC.IND.relation.FRST The indiscernibility relation based on fuzzy rough set theory
BC.boundary.reg.RST Computation of a boundary region
BC.negative.reg.RST Computation of a negative region
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
D.discretize.equal.intervals.RST Unsupervised discretization into intervals of equal length.
D.global.discernibility.heuristic.RST Supervised discretization based on the maximum discernibility heuristic
BC.positive.reg.RST Computation of a positive region
BC.positive.reg.FRST Positive region based on fuzzy rough set
C.POSNN.FRST The positive region based fuzzy-rough nearest neighbor algorithm
FS.greedy.heuristic.superreduct.RST The greedy heuristic method for determining superreduct based on RST
D.discretization.RST The wrapper function for discretization methods
FS.nearOpt.fvprs.FRST The near-optimal reduction algorithm based on fuzzy rough set theory
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
MV.mostCommonValResConcept The most common value or mean of an attribute restricted to a concept
RI.AQRules.RST Rule induction using the AQ algorithm
D.discretize.quantiles.RST The quantile-based discretization
C.FRNN.FRST The fuzzy-rough nearest neighbor algorithm
MV.missingValueCompletion Wrapper function of missing value completion
D.local.discernibility.heuristic.RST Supervised discretization based on the local discernibility heuristic
MV.conceptClosestFit Concept Closest Fit
IS.FRPS.FRST The fuzzy rough prototype selection method
FS.quickreduct.FRST The fuzzy QuickReduct algorithm based on FRST
RI.CN2Rules.RST Rule induction using a version of CN2 algorithm
FS.quickreduct.RST QuickReduct algorithm based on RST
FS.permutation.heuristic.reduct.RST The permutation heuristic algorithm for computation of a decision reduct
C.FRNN.O.FRST The fuzzy-rough ownership nearest neighbor algorithm
FS.one.reduct.computation Computing one reduct from a discernibility matrix
FS.DAAR.heuristic.RST The DAAR heuristic for computation of decision reducts
FS.reduct.computation The reduct computation methods based on RST and FRST
RI.GFRS.FRST Generalized fuzzy rough set rule induction based on FRST
X.nOfConflicts The discernibility measure
SF.asDecisionTable Converting a data.frame into a DecisionTable object
X.laplace Rule voting by the Laplace estimate
SF.applyDecTable Apply for obtaining a new decision table
FS.all.reducts.computation A function for computing all decision reducts of a decision system
IS.FRIS.FRST The fuzzy rough instance selection algorithm
RoughSets-package Getting started with the RoughSets package
RoughSetData Data set of the package
X.entropy The entropy measure
X.gini The gini-index measure
predict.RuleSetFRST The predicting function for rule induction methods based on FRST
MV.deletionCases Missing value completion by deleting instances
SF.asFeatureSubset Converting custom attribute name sets into a FeatureSubset object
SF.read.DecisionTable Reading tabular data from files.
MV.mostCommonVal Replacing missing attribute values by the attribute mean or common values
print.RuleSetRST The print function for RST rule sets
RI.laplace Quality indicators of RST decision rules
X.rulesCounting Rule voting by counting matching rules
RI.indiscernibilityBasedRules.RST Rule induction from indiscernibility classes.
print.FeatureSubset The print method of FeatureSubset objects
X.ruleStrength Rule voting by strength of the rule
summary.LowerUpperApproximation The summary function of lower and upper approximations based on RST and FRST
predict.RuleSetRST Prediction of decision classes using rule-based classifiers.
summary.PositiveRegion The summary function of positive region based on RST and FRST
MV.globalClosestFit Global Closest Fit
RI.LEM2Rules.RST Rule induction using the LEM2 algorithm
summary.RuleSetFRST The summary function of rules based on FRST
summary.RuleSetRST The summary function of rules based on RST
RI.hybridFS.FRST Hybrid fuzzy-rough rule and induction and feature selection
as.character.RuleSetRST The as.character method for RST rule sets
as.list.RuleSetRST The as.list method for RST rule sets
summary.IndiscernibilityRelation The summary function for an indiscernibility relation
[.RuleSetRST The [. method for "RuleSetRST" objects
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License GPL (>= 2)
Encoding UTF-8
URL https://github.com/janusza/RoughSets
Date 2019-12-15
LinkingTo Rcpp
RoxygenNote 6.1.0
NeedsCompilation yes
Packaged 2019-12-14 23:22:40 UTC; bergmeir
Repository CRAN
Date/Publication 2019-12-15 06:30:19 UTC

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