A modified constructive fuzzy neural networks for classification of large-scale and complicated data

Authors:
Lunwen Wang;Yanhua Wu;Ying Tan;Ling Zhang
Affiliations:
702 Research Division of Electronic Engineering Institute, Hefei, China;702 Research Division of Electronic Engineering Institute, Hefei, China;University of Science and Technology of China, Hefei, China;Institute of Artificial intelligence, Anhui University, Hefei, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

Citing 3
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Abstract

Constructive fuzzy neural networks (i.e., CFNN) proposed in [1] cannot be used for non-numerical data. In order to use CFNN to deal with non-numerical complicated data, rough set theory is adopted to improve the CFNN in this paper. First of all, we use rough set theory to extract core set of non-numerical attributes and decrease number of dimension of samples by reducing redundancy. Secondly, we can pre-classify the samples according to non-numerical attributes. Thirdly, we use CFNN to classify the samples according to numerical attributes. The proposed method not only increases classification accuracy but also speeds up classification process. Finally, the classification of wireless communication signals is given as an example to illustrate the validation of the proposed method in this paper.