TY - CONF
T1 - Ordered Weighted Average Based Fuzzy Rough Sets
AU - Jensen, Richard
AU - Verbiest, Nele
AU - Cornelis, Chris
N1 - C. Cornelis, N. Verbiest and R. Jensen. Ordered Weighted Average Based Fuzzy Rough Sets. Proceedings of the 5th International Conference on Rough Sets and Knowledge Technology (RSKT2010), pp. 78-85, 2010.
PY - 2010/10
Y1 - 2010/10
N2 - 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.
AB - 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.
M3 - Paper
SP - 78
EP - 85
ER -