Multi-Label Classification Based on Multi-Objective Optimization

Multi-label classification refers to the task of predicting potentially multiple labels for a given instance. Conventional multi-label classification approaches focus on 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 trade-offs among multiple inconsistent objectives, such as minimizing Hamming Loss while maximizing Micro F1. In this article, 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 inconsistent, even 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 different trade-offs among multiple objectives. So users can flexibly construct various predictive models from the solution set, which provides 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 by optimizing over multiple objectives simultaneously.

Authors

• Chuan Shi Beijing University of Posts and Telecommunications
• Xiangnan Kong
• Di Fu
• Philip S. Yu University of Illinois, Chicago
• Bin Wu Beijing University of Posts and Telecommunications

Keywords

• Classification
• classifier design and evaluation
• model selection
• multi-label classification
• multi-objective optimization
• pattern analysis