Abstract
In this paper a modified fuzzy min-max neural network model for pattern classification and feature extraction is described. We define a new hypercube membership function which has a weight factor to each of the feature within a hyperbox. The weight factor makes it possible to consider the degree of relevance of each feature to a class during the classification process. Based on the proposed model, a knowledge extraction method is presented. In this method, a list of relevant features for a given class is extracted from the trained network using the hyperbox membership functions and connection weights. For this purpose we define a Relevance Factor that represents a degree of relevance of a feature to the given class and a similarity measure between fuzzy membership functions of the hyperboxes. Experimental results for the proposed methods and discussions are presented for the evaluation of the effectiveness and feasibility of the proposed methods.
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© 2004 Springer-Verlag Berlin Heidelberg
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Kim, H.J., Ryu, T.W., Nguyen, T.T., Lim, J.S., Gupta, S. (2004). A Weighted Fuzzy Min-Max Neural Network for Pattern Classification and Feature Extraction. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds) Computational Science and Its Applications – ICCSA 2004. ICCSA 2004. Lecture Notes in Computer Science, vol 3046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24768-5_85
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DOI: https://doi.org/10.1007/978-3-540-24768-5_85
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22060-2
Online ISBN: 978-3-540-24768-5
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