An architecture for constructing fuzzy regression tree forests using opt-aiNet

Abstract

This paper presents a new approach to combining multiple fuzzy regression trees, which are induced by applying the modified Elgasir fuzzy regression tree algorithm. This method utilises Trapezoidal membership functions for fuzzification and the Takagi-Sugeno fuzzy inference to obtain the final predicted values. A modified version of Artificial Immune Network model (opt-aiNet) is used for the simultaneous optimization of the membership functions across all trees within the forest. Boston housing and Abalone are two real-world datasets from the UCI repository used to evaluate the proposed approach. The empirical results have showed that fuzzy regression tree forests reduce the error rate compared with single fuzzy regression tree.