Chris Cornelis Ghent University, Department of Applied Mathematics, Computer Science and Statistics Keynote title: Hybridization of Fuzzy Sets and Rough Sets: Achievements and Opportunities

Abstract

Fuzzy rough sets emerge as a combination of fuzzy sets (Zadeh, 1965) and rough sets (Pawlak, 1982): while the former model vague information by recognizing that membership to certain concepts is a matter of degree, the latter handle potentially inconsistent information by providing a lower and upper approximation of a concept. Both frameworks can be integrated from various perspectives, and the applications of the hybrid theory span a wide array of machine learning problems including classification and feature selection. In this talk, I will provide a gentle introduction to fuzzy-rough hybridization, overview the state-of-the-art in this domain and identify some important opportunities for further development.