## Hybridization of Fuzzy Sets and Rough Sets: Achievements and Opportunities

### Chris Cornelis

DOI: http://dx.doi.org/10.15439/2022F302

Citation: Proceedings of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 30, pages 7–14 (2022)

Abstract. Fuzzy rough sets are the fruit of an intense and longlasting collaboration effort between fuzzy set theory and rough set theory. Seminal research on the hybridization originated in the late 1980's, and has inspired generations of researchers from around the globe to address both theoretical and practical challenges. In this paper, we gauge the state-of-the-art in this domain and identify opportunities for further development. In particular, we highlight the potential of fuzzy quantifiers in creating new robust fuzzy rough models, we advocate closer integration with granular computing as a stepping stone for designing rule induction algorithms, and we contemplate the role of fuzzy rough sets vis-à-vis explainable artificial intelligence.

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