This paper works on fuzzy min-max classifier neural network implementation. Fuzzy min-max classifier creates hyperboxes for classification. Each fuzzy set hyperbox is an n-dimensional pattern space defined by a min point and max point with a corresponding membership function. The fuzzy min-max algorithm is used to determine the min-max points of the hyperbox. The use of a fuzzy set approach to pattern classification inherently provides degree of membership information that is extremely useful in higher level decision making. A confidence factor is calculated for every FMM hyperbox, and a threshold value defined by user is used to prune the hyperboxes with low confidence factors. This will describe the relationships between fuzzy sets and pattern classification.
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