R package 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.
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Mardani, Abbas; Nilashi, Mehrbakhsh; Antucheviciene, Jurgita; Tavana, Madjid; Bausys, Romualdas; Ibrahim, Othman: Recent fuzzy generalisations of rough sets theory: a systematic review and methodological critique of the literature (2017)
- Chen, Hongmei; Li, Tianrui; Cai, Yong; Luo, Chuan; Fujita, Hamido: Parallel attribute reduction in dominance-based neighborhood rough set (2016)
- Li, Hua; Li, Deyu; Zhai, Yanhui; Wang, Suge; Zhang, Jing: A novel attribute reduction approach for multi-label data based on rough set theory (2016)