RoughSets1.3-8 package

Data Analysis Using Rough Set and Fuzzy Rough Set Theories

The `as.character`

method for RST rule sets

The `as.list`

method for RST rule sets

Computation of a boundary region

The decision-relative discernibility matrix based on fuzzy rough set t...

Computation of a decision-relative discernibility matrix based on the ...

The indiscernibility relation based on fuzzy rough set theory

Computation of indiscernibility classes based on the rough set theory

The fuzzy lower and upper approximations based on fuzzy rough set theo...

Computation of lower and upper approximations of decision classes

Computation of a negative region

Positive region based on fuzzy rough set

Computation of a positive region

The fuzzy-rough nearest neighbor algorithm

The fuzzy-rough ownership nearest neighbor algorithm

The positive region based fuzzy-rough nearest neighbor algorithm

The wrapper function for discretization methods

Unsupervised discretization into intervals of equal length.

The quantile-based discretization

Supervised discretization based on the maximum discernibility heuristi...

Supervised discretization based on the local discernibility heuristic

A function for computing all decision reducts of a decision system

The DAAR heuristic for computation of decision reducts

The superreduct computation based on RST and FRST

The greedy heuristic algorithm for computing decision reducts and appr...

The greedy heuristic method for determining superreduct based on RST

The near-optimal reduction algorithm based on fuzzy rough set theory

Computing one reduct from a discernibility matrix

The permutation heuristic algorithm for computation of a decision redu...

The fuzzy QuickReduct algorithm based on FRST

QuickReduct algorithm based on RST

The reduct computation methods based on RST and FRST

The fuzzy rough instance selection algorithm

The fuzzy rough prototype selection method

Concept Closest Fit

Missing value completion by deleting instances

Global Closest Fit

Wrapper function of missing value completion

Replacing missing attribute values by the attribute mean or common val...

The most common value or mean of an attribute restricted to a concept

The predicting function for rule induction methods based on FRST

Prediction of decision classes using rule-based classifiers.

The print method of FeatureSubset objects

The print function for RST rule sets

Rule induction using the AQ algorithm

Rule induction using a version of CN2 algorithm

Generalized fuzzy rough set rule induction based on FRST

Hybrid fuzzy-rough rule and induction and feature selection

Rule induction from indiscernibility classes.

Quality indicators of RST decision rules

Rule induction using the LEM2 algorithm

Getting started with the RoughSets package

Apply for obtaining a new decision table

Converting a data.frame into a `DecisionTable`

object

Converting custom attribute name sets into a FeatureSubset object

Reading tabular data from files.

The `[.`

method for `"RuleSetRST"`

objects

The summary function for an indiscernibility relation

The summary function of lower and upper approximations based on RST an...

The summary function of positive region based on RST and FRST

The summary function of rules based on FRST

The summary function of rules based on RST

The entropy measure

The gini-index measure

Rule voting by the Laplace estimate

The discernibility measure

Rule voting by counting matching rules

Rule voting by strength of the rule

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 ZdzisĹ‚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.

Maintainer: Christoph Bergmeir License: GPL (>= 2) Last published: 2024-01-23