Rough set decision algorithms for modeling with uncertainty

The use of decision rules allows to extract information and to infer conclusions from relational databases in a reliable way, thanks to some indicators like support and certainty. Moreover, decision algorithms collect a group of decision rules that satisfies desirable properties to describe the rela...

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Bibliographic Details
Published in:Journal of computational and applied mathematics 2024-02, Vol.437, p.115413, Article 115413
Main Authors: Chacón-Gómez, Fernando, Cornejo, M. Eugenia, Medina, Jesús, Ramírez-Poussa, Eloísa
Format: Article
Language:English
Subjects:
Decision algorithms
Decision rules
Fuzzy rough set theory
Rough set theory
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Summary:The use of decision rules allows to extract information and to infer conclusions from relational databases in a reliable way, thanks to some indicators like support and certainty. Moreover, decision algorithms collect a group of decision rules that satisfies desirable properties to describe the relational system. However, when a decision table is considered within a fuzzy environment, it is necessary to extend all notions related to decision algorithms to this framework. This paper presents a generalization of these notions, highlighting the new definitions of indicators of relevance to describe decision rules and decision algorithms.
ISSN:0377-0427
1879-1778
DOI:10.1016/j.cam.2023.115413