According to traditional rough set theory approach, attribute reduction methods are performed on the decision tables with the discretized value domain, which are decision tables obtained by discretized data methods. In recent years, researches have proposed methods based on fuzzy rough set approach to solve the problem of attribute reduction in decision tables with numerical value domain. In this paper, we proposeafuzzy distance between two partitions and an attribute reduction method in numerical decision tables based on proposed fuzzy distance. Experiments on data sets show that the classification accuracy of proposed method is more efficient than the ones based fuzzy entropy.
If the inline PDF is not rendering correctly, you can download the PDF file here.
1. Dubois, D., H. Prade. Rough Fuzzy Sets and Fuzzy Rough Sets. - International Journal of General Systems, Vol. 17, 1990, pp. 191-209.
2. Demetrovics, J., V.D. Thi, N. L. Giang. An Efficient Algorithm for Determining the Set of All Reductive Attributes in Incomplete Decision Tables. - Cybernetics and Information Technologies, Vol. 13, 2013, No 4, pp. 118-126.
3. Demetrovics, J., V.D. Thi, N.L. Giang. On Finding All Reducts of Consistent Decision Tables. - Cybernetics and Information Technologies, Vol. 14, 2014, No 4, pp. 3-10.
4. Demetrovics, J., N. Thi, L. Huong, V.D. Thi, N.L. Giang. Metric Based Attribute Reduction Method in Dynamic Decision Tables. - Cybernetics and Information Technologies, Vol. 16, 2016, No 2, pp. 3-15.