Evolutionary Label Selection for Multi-label Classification

Multi-label classification is an emerging research area that aims to assign multiple class labels to each object or instance. In the era of big data, numerous multi-label problems, such as gene function prediction, involve a large number of labels, which most multi-label algorithms cannot cope with. One approach to reduce the number of labels is to construct a small set of new labels. However, creating new labels poses the challenge of preserving label meanings. This work proposes to decrease the number of labels by selecting a small subset of original labels, referred to as label selection, which preserves the original label meanings. Since label selection is a discrete optimisation problem with a large search space, we employ evolutionary computation (EC), specifically differential evolution, to achieve label selection. The contribution of this work lies in establishing a foundation for EC-based label selection, encompassing representation, fitness function, and the testing process. Experimental results demon-strate that the proposed evolutionary label selection algorithm significantly reduces the number of labels while maintaining or even improving performance compared to using all labels. Furthermore, the proposed algorithm exhibits high stability in terms of classification performance and the selected labels.