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| Feature Selection Based on NeuroFuzzy Networks |
| SANG Nong1, XIE YanTao1, GAO RuXin1, ZHANG TianXu2 |
1.Institute for Pattern Recognition and Artificial Intelligence, Huazhong University of Science and Technology, Wuhan 430074
2.Key Laboratory of Ministry of Education for Image Processing and Intelligent Control, Huazhong University of Science and Technology, Wuhan 430074 |
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Abstract To some extent, the feature selection algorithms based on artificial neural networks can be regarded as the special cases of the architecture pruning algorithms. However, they usually require preprocessing of data normalization, which may change the distribution of the original data which is important to the classification. Neurofuzzy networks are fuzzy inference systems with selfstudy ability. In this paper it is combined with the architecture pruning algorithm based on membership space and a new feature selection algorithm is proposed. The membership functions of the algorithm are learned adaptively, and the learning process is finished before the feature selection. Experiments on natural and synthesized data are given and compared with some traditional techniques. The results show that the proposed method is superior to the traditional ones.
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Received: 18 November 2005
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2006, Vol. 19 
