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A Weighted Fuzzy Min-Max Neural Network for Pattern Classification  

Kim Ho-Joon (한동대학교 전산전자공학부)
Park Hyun-Jung (삼성전자 프린팅사업부)
Publication Information
Journal of KIISE:Software and Applications / v.33, no.8, 2006 , pp. 692-702 More about this Journal
Abstract
In this study, a weighted fuzzy min-max (WFMM) neural network model for pattern classification is proposed. The model has a modified structure of FMM neural network in which the weight concept is added to represent the frequency factor of feature values in a learning data set. First we present in this paper a new activation function of the network which is defined as a hyperbox membership function. Then we introduce a new learning algorithm for the model that consists of three kinds of processes: hyperbox creation/expansion, hyperbox overlap test, and hyperbox contraction. A weight adaptation rule considering the frequency factors is defined for the learning process. Finally we describe a feature analysis technique using the proposed model. Four kinds of relevance factors among feature values, feature types, hyperboxes and patterns classes are proposed to analyze relative importance of each feature in a given problem. Two types of practical applications, Fisher's Iris data and Cleveland medical data, have been used for the experiments. Through the experimental results, the effectiveness of the proposed method is discussed.
Keywords
Feature selection; Pattern Classification; Fuzzy min-max neural network; Fuzzy; Hyperbox;
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