A Survey on Co-Evolutionary Learning Algorithm for Addressing Multilabel Classification Problem

Multi-label classification refers to the task of predicting potentially multiple labels for a given instance. Conventional multi-label classification approaches focus on the single objective setting, where the learning algorithm optimizes over a single performance criterion (e.g.Ranking Loss) or a heuristic function. The basic assumption is that the optimization over one single objective can improve the overall performance of multi-label classification and meet the requirements of various applications. However, in many real applications, an optimal multi-label classifier may need to consider the tradeos among multiple conflicting objectives, such as minimizing Hamming Loss and maximizing Micro F1. In this paper, we study the problem of multi-objective multi-label classification and propose a novel solution (called Moml) to optimize over multiple objectives simultaneously. Note that optimization objectives may be conflicting, thus one cannot identify a single solution that is optimal on all objectives.