Ordered Weighted Average Based Fuzzy Rough Sets

Richard Jensen, Nele Verbiest, Chris Cornelis

Research output: Contribution to conferencePaperpeer-review

62 Citations (SciVal)
120 Downloads (Pure)

Abstract

Traditionally, membership to the fuzzy-rough lower, resp. upper approximation is determined by looking only at the worst, resp. best performing object. Consequently, when applied to data analysis problems, these approximations are sensitive to noisy and/or outlying samples. In this paper, we advocate a mitigated approach, in which membership to the lower and upper approximation is determined by means of an aggregation process using ordered weighted average operators. In comparison to the previously introduced vaguely quantified rough set model, which is based on a similar rationale, our proposal has the advantage that the approximations are monotonous w.r.t. the used fuzzy indiscernibility relation. Initial experiments involving a feature selection application confirm the potential of the OWA-based model.
Original languageEnglish
Pages78-85
Number of pages8
Publication statusPublished - Oct 2010

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