Multiobjective Fuzzy Genetics-based Machine Learning with Accuracy-Oriented Pre-Optimization

Abstract

Fuzzy classifier design requires maximization of classification accuracy and minimization of complexity. Multiobjective Fuzzy Genetics-Based Machine Learning (MoFGBL) can efficiently obtain a set of fuzzy classifiers considering the above-mentioned two objectives at the same time using an evolutionary multiobjective optimization algorithm. However, the search by MoFGBML is biased to minimize the complexity, and it is easy to obtain classifiers with low complexity. At the same time, it is difficult to obtain classifiers with high classification accuracy. In this paper, we propose a two-stage MoFGBML, which first performs accuracy-oriented single-objective optimization to obtain a set of accurate classifiers with a large number of rules. Then, multiobjective optimization is performed to obtain a wide variety of classifiers, from highly-accurate ones to simple ones.