Comparison between Multi-objective and Many-objective Formulations in Fuzzy Genetics-based Machine Learning for Multi-label Classification

Abstract

In some real-world data mining applications, multiple class labels are assigned to a single pattern. Classification problems with these patterns are said to be multi-label classification. Recently, some interpretable classifier design algorithms have been proposed for multi-label classification. Multi-objective fuzzy genetics-based machine learning is one of the most interpretable classifier design algorithms. This algorithm optimizes a number of fuzzy rule-based classifiers based on maximizing accuracy and minimizing complexity. Because there are several accuracy measures for multi-label classification, multiple runs with a different measure are necessary when we want to obtain the best classifier in terms of each of those accuracy measures. In this paper, we compare three two-objective formulations (i.e., one accuracy measure and one complexity measure) and a four-objective formulation (i.e., three accuracy measures and one complexity measure) in fuzzy genetics-based machine learning for multi-label classification.