On application of artificial immune system to optimize fuzzy regression trees

Abstract

This paper presents the application of a novel fuzzy regression trees technique to real-world regression problems. Elgasir algorithm is a fuzzy regression trees technique applied to crisp regression trees in order to overcome the problems of sharp decision boundaries. Fuzzy regression trees are induced by applying Elgasir algorithm to crisp CHAID regression trees based on Trapezoidal membership functions and Takagi-Sugeno fuzzy inference. Elgasir algorithm associated with artificial immune system are used to induce the optimized version of Elgasir algorithm. The Elevators and Compactiv are two real-world datasets from KEEL repository used to perform empirical evaluation for the proposed method. The Elevators dataset has been retrieved from the task of controlling a F16 aircraft. The Compactiv is computer Activity dataset. The empirical results showed show the capability of Elgasir optimized to produce robust fuzzy regression trees.