Multifactorial Genetic Fuzzy Data Mining for Building Membership Functions
Ting‐Chen Wang, Rung-Tzuo Liaw · 2020
Association mining is a famous data mining technology because its form is explainable by human beings. Innovating fuzzy set theory to associations mining provides a solution to quantitative database, where membership function plays an important role in mining fuzzy associations. Genetic algorithm (GA) has been successfully applied to the optimization of membership functions. Based on the spirit of divide-and-conquer, GA optimizes the membership functions for each item separately. Nevertheless, the cooperation among different items in the course of evolution was never considered. Evolutionary multitasking optimization (EMO) is an emerging searching paradigm which dedicates to solving multiple tasks simultaneously for improving the search efficiency. This study introduces the EMO into genetic fuzzy data mining to address the above issue. Specifically, this study incorporates a state-of the-art genetic fuzzy data mining method, the structure-based representation genetic algorithm, with the well-known multifactorial evolutionary algorithm (MFEA). A series of experiments is conducted to validate the effectiveness and efficiency of the proposed method. The results indicate that the proposed method improves the structure-based representation genetic algorithm in terms of convergence speed and solution quality on all sizes of datasets. The results also show that the proposed method is about 20 times faster than the structure-based representation genetic algorithm with respect to the exploited number of evaluations.