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http://dx.doi.org/10.3745/KTSDE.2013.2.5.341

A Rule Extraction Method Using Relevance Factor for FMM Neural Networks  

Lee, Seung Kang (한동대학교 정보통신공학과)
Lee, Jae Hyuk (한동대학교 전산전자공학부)
Kim, Ho Joon (한동대학교 전산전자공학부)
Publication Information
KIPS Transactions on Software and Data Engineering / v.2, no.5, 2013 , pp. 341-346 More about this Journal
Abstract
In this paper, we propose a rule extraction method using a modified Fuzzy Min-Max (FMM) neural network. The suggested method supplements the hyperbox definition with a frequency factor of feature values in the learning data set. We have defined a relevance factor between features and pattern classes. The proposed model can solve the ambiguity problem without using the overlapping test process and the contraction process. The hyperbox membership function based on the fuzzy partitions is defined for each dimension of a pattern class. The weight values are trained by the feature range and the frequency of feature values. The excitatory features and the inhibitory features can be classified by the proposed method and they can be used for the rule generation process. From the experiments of sign language recognition, the proposed method is evaluated empirically.
Keywords
FMM Neural Network; Rule Extraction; Pattern Recognition;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 B. Gabrys and A. Bargiela, "General fuzzy min-max neural network for clustering and classification," IEEE Transaction on Neural Networks, Vol.11, No.3, pp.769-783, 2000.   DOI   ScienceOn
2 P. K. Simpson, "Fuzzy min-max neural network - Part1 : Classification," IEEE Transaction on Neural Network, Vol.3, No.5, pp.776-786, 1992.   DOI   ScienceOn
3 A. Quteishat, C. P. Lim, and K. S. Tan, "A modified fuzzy min-max neural network with a genetic algorithm-based rule extractor," IEEE Transaction on System, Man, and Cybernetics-Part A: System and Humans, Vol.40, No.3, pp.641-650, 2010.   DOI   ScienceOn
4 H. J. Kim, "Two-Stage Neural Networks for Sign Language Pattern Recognition", Journal of Korean Institute of Intelligent Systems, Vol.22, No.3, pp.319-327, 2012.   과학기술학회마을   DOI   ScienceOn
5 M. M. Zaki and S. I. Shaheen, "Sign language recognition using a combination of new vision based features," Pattern Recognition Letters, Vol.32, No.4, pp.572-577, 2011.   DOI   ScienceOn
6 C. Garcia and M. Delakis "Convolutional face finder: A neural architecture for fast and robust face detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.26, No.11, pp.1408-1423, 2004.   DOI   ScienceOn
7 R. Yang and S. Sarkar, "Coupled grouping and matching for sign and gesture recognition," Computer Vision and Image Understanding, Vol.113, No.6, pp.663-681, 2009.   DOI   ScienceOn
8 D. Weinland, R. Ronfard and E. Boyer, "Free viewpoint action recognition using motion history volumes," Computer Vision and Image Understanding, Vol.104, pp.249-257, 2006.   DOI   ScienceOn
9 A. Yilmaz and M. Shah, "Actions sketch: A novel action representation," IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, pp.984-989.
10 R. A. Fisher "The use of multiple measurements in taxonomic problems," Annals of Eugenics, Vol.7, No.2, pp.179-188, 1936.   DOI