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Data Mining Based on Computational Intelligence
引用本文:WANGYuan-zhen ZHANGZhi-bing YIBao-lin LIHua-yang. Data Mining Based on Computational Intelligence[J]. 武汉大学学报:自然科学英文版, 2005, 10(2): 371-374. DOI: 10.1007/BF02830669
作者姓名:WANGYuan-zhen ZHANGZhi-bing YIBao-lin LIHua-yang
作者单位:[1]CollegeofComputerScienceandTechnology,HuazhongUniversityofScienceandTechnology,Wuhan430074,Hubei,China [2]SchoolofComputerScienceandTechnology,HuazhongNormalUniversity,Wuhan130079,Hubei,China
基金项目:Supported by the National Research Foundation for the Doctoral Program of Higher Education of China (20030487032)
摘    要:This paper combines computational intelligence tools: neural network, fuzzy logic, and genetic algorithm to develop a data mining architecture (NFGDM). which discovers patterns and represents them in understandable forms. In the NFGDM. input data are preprocessed by fuzzification, the preprocessed data of input variables are then used to train a radial Basis probabilistic neural network to classify the dataset according to the classes considered. A rule extraction technique is then applied in order to extract explicit knowledge from the trained neural networks and represent it in the form of fuzzy if-then rules. In the final stage, genetic algorithm is used as a rule-pruning module to eliminate those weak rules that are still in the rule bases. Comparison with some known neural network classifier, the architecture has fast learning speed, and it is characterized hy the incorporation of the possibillty information into the consequents of classification rules in human understandable forms. The experiments show that the NFGDM is more efficient and more robust than traditional decision tree method.

关 键 词:数据隐藏 人工神经网络 模糊逻辑技术 遗传算法 计算机技术
收稿时间:2004-03-01

Data mining based on computational intelligence
Wang Yuan-zhen,Zhang Zhi-bing,Yi Bao-lin,Li Hua-yang. Data mining based on computational intelligence[J]. Wuhan University Journal of Natural Sciences, 2005, 10(2): 371-374. DOI: 10.1007/BF02830669
Authors:Wang Yuan-zhen  Zhang Zhi-bing  Yi Bao-lin  Li Hua-yang
Affiliation:(1) College of Computer Science and Technology, Huazhong University of Science and Technology, 430071 Wuhan, Hubei, China;(2) School of Computer Science and Technology, Huazhong Normal University, 430079 Wuhan, Hubei, China
Abstract:This paper combines computational intelligence tools: neural network, fuzzy logic, and genetic algorithm to develop a data mining architecture (NFGDM), which discovers patterns and represents them in understandable forms. In the NFGDM, input data are preprocessed by fuzzification, the preprocessed data of input variables are then used to train a radial basis probabilistic neural network to classify the dataset according to the classes considered. A rule extraction technique is then applied in order to extract explicit knowledge from the trained neural networks and represent it in the form of fuzzy if|then rules. In the final stage, genetic algorithm is used as a rule|pruning module to eliminate those weak rules that are still in the rule bases. Comparison with some known neural network classifier, the architecture has fast learning speed, and it is characterized by the incorporation of the possibility information into the consequents of classification rules in human understandable forms. The experiments show that the NFGDM is more efficient and more robust than traditional decision tree method.
Keywords:data mining  rule extraction  neural network  fuzzy logic  genetic algorithm
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