Association Models to Select the Best Rules for Fuzzy Inference System

Abstract

In the past decade, the fuzzy inference system (FIS) has seen explosive growth in popularity among the researchers and industrialists. The fuzzy classification system is based on the most knowing process of the FIS in order to map features as inputs to outputs classes. Generally, to make a fuzzy classifier decision it is necessary to select manually the best rules that require the intervention of experts. In the current paper, we propose an automatic method to select the best rules using associations models: Apriority and Filter Associations. The proposed method process in five steps: preprocessing (feature reduction), determining membership function for every input and output, nominal representation of the database basing on the membership functions, call for the association model to select the most important rules, and finally post-processing of the obtained rules. In this work, we lead to appropriate rules set tested on the iris data for the classification task. In addition, the proposed method is highly simple to be implemented to control the even complex system. Our system achieved promising results, which demonstrate the effectiveness of the proposed approach.