Heuristics to model the dependencies between Features in Fuzzy Pattern Matching

Fuzzy pattern matching technique represents a group of fuzzy methods for supervised fuzzy pattern recognition. These methods build a prototype for each feature and combine the partial estimations of each prototype by a fusion operator. One of the major problems of this technique is that it is not able to model the dependencies between features, and nowadays there is no heuristic in the literature that solves this problem. In this paper we propose a solution to this problem. In order to keep the good properties of fuzzy pattern matching, this heuristic will have the objective of minimizing the dependencies between features modelled. To show the accuracy of the proposed solution, we have tested the method on several data sets. In this paper, we present the results obtained in a simulated data set and a real data set.