Multiobjective fuzzy genetics-based machine learning for multi-label classification

In multi-label classification problems, multiple class labels are assigned to each instance. Two approaches have been studied in the literature. One is a data transformation approach, which transforms a multi-label dataset into a number of singlelabel datasets. However, this approach often loses the correlation information among classes in the multi-class assignment. The other is a method adaptation approach where a conventional classification method is extended to multi-label classification. Recently, some explainable classification models for multi-label classification have been proposed. Their high interpretability has also been discussed with respect to the transparency of the classification process. Although the explainability is a well-known advantage of fuzzy systems, their applications to multi-label classification have not been well studied. Since multi-label classification problems often have vague class boundaries, fuzzy systems seem to be a promising approach to multi-label classification. In this paper, we propose a new multiobjective evolutionary fuzzy system, which can be categorized as a method adaptation approach. The proposed algorithm produces nondominated classifiers with different tradeoffs between accuracy and complexity. We examine the behavior of the proposed algorithm using synthetic multi-label datasets. We also compare the proposed algorithm with five representative algorithms. Our experimental results on real-world datasets show that the obtained fuzzy classifiers with a small number of fuzzy rules have high transparency and comparable generalization ability to the other examined multi-label classification algorithms.