Induction of Fuzzy-Rule-Based Classifiers With Evolutionary Boosting Algorithms
María José del Jesús, Frank Hoffmann, L. JuncoNavascues, Luciano Sánchez · IEEE Transactions on Fuzzy Systems · 2004
This paper proposes a novel Adaboost algorithm to learn fuzzy-rule-based classifiers. Connections between iterative learning and boosting are analyzed in terms of their respective structures and the manner these algorithms address the cooperation-competition problem. The results are used to explain some properties of the former method. The evolutionary boosting scheme is applied to approximate and descriptive fuzzy-rule bases. The advantages of boosting fuzzy rules are assessed by performance comparisons between the proposed method and other classification schemes applied on a set of benchmark classification tasks.