Elgasir: an algorithm for creating fuzzy regression trees

Authors:
Fathi Gasir;Zuhair Bandar;Keeley Crockett
Affiliations:
The Intelligent Systems Group, Department of Computing and Maths, Manchester Metropolitan University, Manchester, UK;The Intelligent Systems Group, Department of Computing and Maths, Manchester Metropolitan University, Manchester, UK;The Intelligent Systems Group, Department of Computing and Maths, Manchester Metropolitan University, Manchester, UK
Venue:
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Year:
2009

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Abstract

This paper presents a new fuzzy regression tree algorithm known as Elgasir, which is based on the CHAID regression tree algorithm and Takagi-Sugeno fuzzy inference. The Elgasir algorithm is applied to crisp regression trees to produce fuzzy regression trees in order to soften sharp decision boundaries inherited in crisp trees. Elgasir generates a fuzzy rule base by applying fuzzy techniques to crisp regression trees using Trapezoidal membership functions. Then Takagi-Sugeno fuzzy inference is used to aggregate the final output from the fuzzy implications. The approach is evaluated using two problem sets from the UCI repository. Experiments conducted yield an improvement in the performance of fuzzy regression trees compared with crisp CHAID trees. The generated fuzzy regression trees are more robust and presented in a highly visual format which is easy to understand.