Multi-Objective Multi-Label Classification
Chuan Shi, Xiangnan Kong, Philip S. Yu, Bai Wang · 2012
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 multilabel classification and meet the requirements of various applications. However, in many real applications, an optimal multi-label classifier may need to consider the tradeoffs 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 M oml) 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. Our M oml algorithm finds a set of non-dominated solutions which are optimal according to the different tradeoffs of the multiple objectives. So users can flexibly construct various combined predictive models from the solution set, which helps to provide more meaningful classification results in different application scenarios. Empirical studies on real-world tasks demonstrate that the M oml can effectively boost the overall performance of multi-label classification, not limiting to the optimization objectives.