TY - GEN
T1 - A Noise-tolerant Approach to Fuzzy-Rough Feature Selection
AU - Jensen, Richard
AU - Cornelis, Chris
N1 - C. Cornelis and R. Jensen. A Noise-tolerant Approach to Fuzzy-Rough Feature Selection. To appear in: Proceedings of the 17th International Conference on Fuzzy Systems (FUZZ-IEEE'08), 2008.
PY - 2008
Y1 - 2008
N2 - In rough set based feature selection, the goal is to omit attributes (features) from decision systems such that objects in different decision classes can still be discerned. A popular way to evaluate attribute subsets with respect to this
criterion is based on the notion of dependency degree. In the standard approach, attributes are expected to be qualitative;
in the presence of quantitative attributes, the methodology can be generalized using fuzzy rough sets, to handle gradual
(in)discernibility between attribute values more naturally. However, both the extended approach, as well as its crisp
counterpart, exhibit a strong sensitivity to noise: a change in a single object may significantly influence the outcome of the
reduction procedure. Therefore, in this paper, we consider a more flexible methodology based on the recently introduced
Vaguely Quantified Rough Set (VQRS) model. The method can handle both crisp (discrete-valued) and fuzzy (real-valued)
data, and encapsulates the existing noise-tolerant data reduction approach using Variable Precision Rough Sets (VPRS), as well
as the traditional rough set model, as special cases.
AB - In rough set based feature selection, the goal is to omit attributes (features) from decision systems such that objects in different decision classes can still be discerned. A popular way to evaluate attribute subsets with respect to this
criterion is based on the notion of dependency degree. In the standard approach, attributes are expected to be qualitative;
in the presence of quantitative attributes, the methodology can be generalized using fuzzy rough sets, to handle gradual
(in)discernibility between attribute values more naturally. However, both the extended approach, as well as its crisp
counterpart, exhibit a strong sensitivity to noise: a change in a single object may significantly influence the outcome of the
reduction procedure. Therefore, in this paper, we consider a more flexible methodology based on the recently introduced
Vaguely Quantified Rough Set (VQRS) model. The method can handle both crisp (discrete-valued) and fuzzy (real-valued)
data, and encapsulates the existing noise-tolerant data reduction approach using Variable Precision Rough Sets (VPRS), as well
as the traditional rough set model, as special cases.
M3 - Conference Proceeding (Non-Journal item)
BT - 17th International Conference on Fuzzy Systems (FUZZ-IEEE'08)
ER -