Improving fuzzy pattern matching techniques to deal with non discrimination ability features

Fuzzy pattern matching technique represents a group of fuzzy methods for supervised fuzzy pattern recognition. It has a number of advantages over other pattern recognition methods, including simpler methods of feature selection or ability to learn in real time environments, but its main drawback is it is not able to model the correlation between features, since fuzzy pattern matching assumes non interactivity between them. This paper presents an attempt to extend this technique to deal with this kind of features. To show the accuracy of the proposed solution, we present the results obtained in a simulated data set (an extension of the xor problem) and a real data set (the Wisconsin breast cancer data set).