Abstract
Set Pair Analysis (SPA) theory is a relatively new approach to deal with uncertainty, and has been successfully applied to areas including decision making, data fusion, product design, etc. The set pair is defined as a pair that consists of two interrelated sets, SPA’s main idea is considering the relation of certainties and uncertainties of the set pair, then analyzing and processing the relation. In this study, we employ SPA theory in pattern recognition to enhance accuracy and speed. Class \(c_j(j=1,2,...,n)\) is divided into several subclasses which are represented as binary connection number, the value of coefficient i is optimized by genetic algorithm. To validate the usefulness of our method, experiments were carried out and the results indicated that accuracy and speed may be improved significantly by using our method.
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