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RoughSets (version 1.3-8)

Data Analysis Using Rough Set and Fuzzy Rough Set Theories

Description

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.

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Install

install.packages('RoughSets')

Monthly Downloads

339

Version

1.3-8

License

GPL (>= 2)

Issues

3

Pull Requests

0

Stars

37

Forks

13

Maintainer

Christoph Bergmeir

Last Published

January 23rd, 2024

Functions in RoughSets (1.3-8)

BC.LU.approximation.RST

Computation of lower and upper approximations of decision classes
BC.discernibility.mat.FRST

The decision-relative discernibility matrix based on fuzzy rough set theory
D.discretize.equal.intervals.RST

Unsupervised discretization into intervals of equal length.
D.discretize.quantiles.RST

The quantile-based discretization
FS.DAAR.heuristic.RST

The DAAR heuristic for computation of decision reducts
BC.positive.reg.FRST

Positive region based on fuzzy rough set
BC.positive.reg.RST

Computation of a positive region
FS.greedy.heuristic.superreduct.RST

The greedy heuristic method for determining superreduct based on RST
FS.all.reducts.computation

A function for computing all decision reducts of a decision system
C.FRNN.O.FRST

The fuzzy-rough ownership nearest neighbor algorithm
C.FRNN.FRST

The fuzzy-rough nearest neighbor algorithm
FS.reduct.computation

The reduct computation methods based on RST and FRST
FS.feature.subset.computation

The superreduct computation based on RST and FRST
D.global.discernibility.heuristic.RST

Supervised discretization based on the maximum discernibility heuristic
BC.discernibility.mat.RST

Computation of a decision-relative discernibility matrix based on the rough set theory
FS.greedy.heuristic.reduct.RST

The greedy heuristic algorithm for computing decision reducts and approximate decision reducts
IS.FRIS.FRST

The fuzzy rough instance selection algorithm
MV.deletionCases

Missing value completion by deleting instances
MV.globalClosestFit

Global Closest Fit
MV.mostCommonValResConcept

The most common value or mean of an attribute restricted to a concept
D.local.discernibility.heuristic.RST

Supervised discretization based on the local discernibility heuristic
BC.negative.reg.RST

Computation of a negative region
FS.nearOpt.fvprs.FRST

The near-optimal reduction algorithm based on fuzzy rough set theory
FS.quickreduct.FRST

The fuzzy QuickReduct algorithm based on FRST
C.POSNN.FRST

The positive region based fuzzy-rough nearest neighbor algorithm
MV.missingValueCompletion

Wrapper function of missing value completion
RI.AQRules.RST

Rule induction using the AQ algorithm
MV.mostCommonVal

Replacing missing attribute values by the attribute mean or common values
FS.quickreduct.RST

QuickReduct algorithm based on RST
RI.LEM2Rules.RST

Rule induction using the LEM2 algorithm
D.discretization.RST

The wrapper function for discretization methods
RI.hybridFS.FRST

Hybrid fuzzy-rough rule and induction and feature selection
SF.asFeatureSubset

Converting custom attribute name sets into a FeatureSubset object
SF.applyDecTable

Apply for obtaining a new decision table
RoughSetData

Data set of the package
RI.CN2Rules.RST

Rule induction using a version of CN2 algorithm
RoughSets-package

Getting started with the RoughSets package
SF.asDecisionTable

Converting a data.frame into a DecisionTable object
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
SF.read.DecisionTable

Reading tabular data from files.
summary.RuleSetFRST

The summary function of rules based on FRST
RI.GFRS.FRST

Generalized fuzzy rough set rule induction based on FRST
summary.RuleSetRST

The summary function of rules based on RST
IS.FRPS.FRST

The fuzzy rough prototype selection method
predict.RuleSetFRST

The predicting function for rule induction methods based on FRST
X.laplace

Rule voting by the Laplace estimate
predict.RuleSetRST

Prediction of decision classes using rule-based classifiers.
X.gini

The gini-index measure
X.entropy

The entropy measure
X.nOfConflicts

The discernibility measure
MV.conceptClosestFit

Concept Closest Fit
print.FeatureSubset

The print method of FeatureSubset objects
print.RuleSetRST

The print function for RST rule sets
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
RI.indiscernibilityBasedRules.RST

Rule induction from indiscernibility classes.
RI.laplace

Quality indicators of RST decision rules
as.character.RuleSetRST

The as.character method for RST rule sets
as.list.RuleSetRST

The as.list method for RST rule sets
X.rulesCounting

Rule voting by counting matching rules
X.ruleStrength

Rule voting by strength of the rule
[.RuleSetRST

The [. method for "RuleSetRST" objects
summary.IndiscernibilityRelation

The summary function for an indiscernibility relation
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.IND.relation.FRST

The indiscernibility relation based on fuzzy rough set theory
BC.boundary.reg.RST

Computation of a boundary region