Abstract
In this paper, a simple, yet effective, modification of the activation characteristic and training method of the general fuzzy min-max neural network is presented. The suggested method supplements the hyperbox definition with a frequency factor of the input training patterns. With this factor, a gain value is calculated based on the conception of the relevance of pattern density with respect to the hyperbox range. Utilizing this value, we propose an alternative training method that is able to replace the hyperbox contraction process. Thus, the classification results are independent of the class order presented in the training set. The effect of the gain value is analyzed and the result indicates that the proposed model is also less sensitive to distorted information.
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© 2007 Springer-Verlag Berlin Heidelberg
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Lee, J.S., Park, JH., Kim, HJ. (2007). An Improved General Fuzzy Min-Max Neural Network for Pattern Classification. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2007. Communications in Computer and Information Science, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74282-1_106
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DOI: https://doi.org/10.1007/978-3-540-74282-1_106
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74281-4
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