Implementing algorithms of rough set theory and fuzzy rough set theory in the R package "roughSets"

Lala Septem Riza, Andrzej Janusz, Christoph Bergmeir, Chris Cornelis, Francisco Herrera, Dominik Ślȩzak, José Manuel Benítez

Research output: Contribution to journalArticleResearchpeer-review

117 Citations (Scopus)


The package RoughSets, written mainly in the R language, provides implementations of methods from the rough set theory (RST) and fuzzy rough set theory (FRST) for data modeling and analysis. It considers not only fundamental concepts (e.g., indiscernibility relations, lower/upper approximations, etc.), but also their applications in many tasks: discretization, feature selection, instance selection, rule induction, and nearest neighbor-based classifiers. The package architecture and examples are presented in order to introduce it to researchers and practitioners. Researchers can build new models by defining custom functions as parameters, and practitioners are able to perform analysis and prediction of their data using available algorithms. Additionally, we provide a review and comparison of well-known software packages. Overall, our package should be considered as an alternative software library for analyzing data based on RST and FRST.

Original languageEnglish
Pages (from-to)68-89
Number of pages22
JournalInformation Sciences
Publication statusPublished - 10 Dec 2014
Externally publishedYes


  • Discretization
  • Feature selection
  • Fuzzy rough set
  • Instance selection
  • Rough set
  • Rule induction

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