RoughSets: 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 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.
| Version: |
1.3-7 |
| Depends: |
Rcpp |
| LinkingTo: |
Rcpp |
| Suggests: |
class |
| Published: |
2019-12-15 |
| Author: |
Andrzej Janusz [aut],
Lala Septem Riza [aut],
Dominik Ślęzak [ctb],
Chris Cornelis [ctb],
Francisco Herrera [ctb],
Jose Manuel Benitez [ctb],
Christoph Bergmeir [ctb, cre],
Sebastian Stawicki [ctb] |
| Maintainer: |
Christoph Bergmeir <c.bergmeir at decsai.ugr.es> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://github.com/janusza/RoughSets |
| NeedsCompilation: |
yes |
| In views: |
MachineLearning |
| CRAN checks: |
RoughSets results |
Documentation:
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