A New Multi-Rules Approach to Improve the Performance of the Chi Fuzzy Rule Classification Algorithm

The Chi algorithm has been used in many applications to obtain a set of fuzzy rules from the data. Its main advantage is that it requires low learning time in relation to others learning algorithms and that makes it a good candidate for tackling Big Data problems. However, this algorithm has some shortcomings, among them, its limited generalization capability, which leads to obtaining a high number of uncovered examples over new unseen examples. An improved version of this algorithm is presented in this paper. The main difference lies in the process of assigning a candidate rule to each example, the original algorithm assigns only one, and in the new proposal, several rules are assigned. The inclusion of more rules improves the generalization capacity of the acquired knowledge, which in turn leads to an improvement in accuracy. The paper includes an extensive experimental study to show the performance of the proposal.