Multi-label Classification: Dealing with Imbalance by Combining Labels

Data imbalance is a common problem both in single-label classification (SLC) and multi-label classification (MLC). There is no doubt that the predicting result suffers from this problem. Although, a broad range of studies associate with imbalance problem, most of them focus on SLC and for MLC is relatively less. Actually, this problem arising in MLCis more frequent and complex than in SLC. In this paper, we proceed from dealing with imbalance problem for MLC and propose a new approach called DEML. DEML transforms the whole label set of multi-label dataset into some subsets and each subset is treated as a multi-class dataset with balanced class distribution, which not only addressing imbalance problem but also preserving dataset integrity and consistency. Extensive experiments show that DEML possesses highly competitive performance both in computation and effectiveness.