Sciweavers

Share
RSKT
2010
Springer

Ordered Weighted Average Based Fuzzy Rough Sets

9 years 10 months ago
Ordered Weighted Average Based Fuzzy Rough Sets
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. Key words: fuzzy rough sets, vaguely quantified rough sets, ordered weighted average, aggregation operators, noise tolerance, data analysis
Chris Cornelis, Nele Verbiest, Richard Jensen
Added 30 Jan 2011
Updated 30 Jan 2011
Type Journal
Year 2010
Where RSKT
Authors Chris Cornelis, Nele Verbiest, Richard Jensen
Comments (0)
books