Affiliations: College of Mathematics and Computer Science, Hebei University, Baoding, China | Key Laboratory of Machine Learning and Computational Intelligence of Hebei Province, Baoding, China | Industrial and Commercial College, Hebei University, Baoding, China
Note: [] Corresponding author. Junhai Zhai, College of Mathematics and Computer Science, Hebei University, Baoding 071002, China. Tel.: +86 3125079351; Fax: +86 3125079630; E-mail: [email protected]
Abstract: Fuzzy rough attribute reduct has been widely used to remove redundant real-valued attributes without discretizing. By now, there are two existing fuzzy rough attribute reduct methods, one is based on dependency function and another based on discernibility matrix. The former proposed by Shen in 2002 can deal with fuzzy decision table (FDT) with real-valued condition attributes and fuzzy decision attributes. However, this algorithm is not convergent on many real datasets, and the computational complexity of the algorithm increases exponentially with the number of input variables. The latter proposed by Tsang in 2008 can only deal with fuzzy decision table with real-valued condition attributes and symbol-valued decision attributes. In this paper, we extend the latter method and propose two algorithms for calculating all fuzzy rough attribute reducts to deal with fuzzy decision table with real-valued condition and decision attributes. The first algorthim is designed for computing all fuzzy attribute reducts, yet the computation complexity of this algorithm increases exponentially with the number of attributes. The second one which can find one near-optimal reduct is a heuristic variant of the first algorithm. The experimental results show the proposed method is feasible and effective.
