Attributes Reduction Using Fuzzy Rough Sets

• Published in: IEEE transactions on fuzzy systems Vol. 16; no. 5; pp. 1130 - 1141
• Main Authors: Tsang, E.C.C., Degang Chen, Yeung, D.S., Xi-Zhao Wang, Lee, J.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2008; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Approximation; Attributes reduction; Computer science; Data analysis; Foundations; fuzzy rough sets; Fuzzy sets; Mathematical analysis; Mathematical models; Mathematics; Operators; Physics; Reduction; Rough sets; Solids; Uncertainty; rough sets; fuzzy rough sets; attributes reduction
• ISSN: 1063-6706; 1941-0034
• DOI: 10.1109/TFUZZ.2006.889960

Fuzzy rough sets are the generalization of traditional rough sets to deal with both fuzziness and vagueness in data. The existing researches on fuzzy rough sets are mainly concentrated on the construction of approximation operators. Less effort has been put on the attributes reduction of databases with fuzzy rough sets. This paper mainly focuses on the attributes reduction with fuzzy rough sets. After analyzing the previous works on attributes reduction with fuzzy rough sets, we introduce formal concepts of attributes reduction with fuzzy rough sets and completely study the structure of attributes reduction. An algorithm using discernibility matrix to compute all the attributes reductions is developed. Based on these lines of thought, we set up a solid mathematical foundation for attributes reduction with fuzzy rough sets. The experimental results show that the idea in this paper is feasible and valid.