Extension of Multi-Objective Fuzzy Genetics-Based Machine Learning for Multi-Label Classification to Many-Objective Optimization

Abstract

Multi-objective fuzzy genetics-based machine learning for multi-label classification called MoFGBMLML is a classifier design method for interpretable fuzzy classifiers. It generates a number of non-dominated fuzzy rule-based classifiers with different accuracy-complexity tradeoffs. In multi-label classification, some performance metrics have been simultaneously used for comparison. However, MoFGBMLML can handle only one performance metric in a single run. In this paper, we extend two-objective MoFGBMLML to many-objective optimization. In the many-objective optimization formulation, we use several performance metrics as objective functions simultaneously. This extension enables MoFGBMLML to obtain multiple optimal classifiers with respect to several performance metrics for multi-label classification in a single run.