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Fuzzy kernel feature selection with multi-objective differential evolution algorithm

In this paper, we propose a multi-objective differential evolution-based filter approach for feature selection that interconnects fuzzy- and kernel-based information theory measures to find feature subsets that are optimal responses to the targets. In contrast to the existing filter approaches using the principles of information theory and rough set theory, our approach can be applied to continuous datasets without discretisation. Moreover, our study is the first in the literature that employs fuzzy and kernel measures to form a filter criterion for feature selection, to our knowledge. We prove various favourable results using a variety of benchmark datasets and also demonstrate that our approach can better search the dimensionality space to reach maximum predictive of the response.