Fuzzy Systems and Knowledge Discovery, Fourth International Conference on (2007)
Haikou, Hainan, China
Aug. 24, 2007 to Aug. 27, 2007
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/FSKD.2007.334
Dongbo Zhang , Xiangtan University, Xiangtan, 411105, China
Yaonan Wang , Hunan University, Changsha
A method to construct fuzzy rough model is proposed. By means of adaptive Gaustafason-Kessel (G-K) clustering algorithm, fuzzy partition can be accomplished and corresponding fuzzy clusters are achieved in data space. Then based on the search of cluster number and attribute subsets through GA search strategy, optimal FRM will be found, and a decision model can be built. The experiment results indicate that FRM method is superior to traditional Bayesian and learning vector quantization (LVQ) methods, moreover, it has more powerful generalization ability. Also, experiment results show that it is favorable to obtain better FRM model if the search of reductive attribute subsets is considered.
Dongbo Zhang, Yaonan Wang, "Fuzzy Rough Modeling Approach: Based on Fuzzy Clustering and GA Search Strategy", Fuzzy Systems and Knowledge Discovery, Fourth International Conference on, vol. 03, no. , pp. 161-166, 2007, doi:10.1109/FSKD.2007.334