Intelligent Classification System Using a Pruned Bayes Fuzzy Rule Set

Abstract

Hybrid intelligent systems which take advantage of the Bayesian/Fuzzy collaboration have been explored in the literature in the last years. Such collaboration can play an important role mainly in real intelligent systems applications, where accuracy and comprehensibility are crucial aspects to be considered. This paper further explore the Bayes Fuzzy method proposing a classification method specially designed to be used in intelligent systems for data analysis. The main idea is to enhance comprehensibility while maintaining accuracy by decreasing the number of fuzzy rules used to explain a Bayesian Classifier (BC). The proposed Pruned Bayes Fuzzy 2 (PBF2) method is based on a new feature selection method named Selection by Markov Blanket Relation Strength (SMBRS). In the performed experiments, PBF2 is empirically applied to a real world police records problem in order to extract a comprehensible and accurate set of rules which can help in crime prevention. The obtained results show PBF2, when used with proper parameters, brings better precision and comprehensibility compared to other Bayesian/Fuzzy-based methods and to C4.5 algorithm.