The Construction of Fuzzy Classification Systems Using the Shuffled Frog Leaping Algorithm

The Construction of Fuzzy Classification Systems Using the Shuffled Frog Leaping Algorithm

Abstract

Various methods have been proposed for constructing and optimizing fuzzy inference systems. This paper first proposes a new method to create zero-order Sugeno fuzzy inference systems using the Shuffled Frog Leaping Algorithm (SFLA). As the second contribution, the paper introduces four improvements over SFLA. The resulting version of SFLA, called ISFLA (Improved SFLA), is also applied to create zero-order Sugeno fuzzy inference systems. We conducted experiments to assess ISFLA and compare it with the original SFLA and three well-known evolutionary algorithms over five standard classification data sets from the UCI machine learning repository. The experimental results show that ISFLA creates fuzzy systems more efficiently than the standard SFLA and some other evolutionary algorithms, i.e., GA, ACO and PSO. Moreover, with respect to the accuracy and the convergence speed criteria, ISFLA and PSO outperform other evolutionary algorithms, while their performance is comparable to each other.

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