Multi-label Classification by Pariwise Training
This paper presents a simple and general approach for multi-label problems. In contrast with the commonly used binary approach, which roots from the 1-vs-rest strategy for multi-class classification, the new method extends the 1-vs-1 strategy and conducts pairwise training. While the binary approach assumes labels are assigned independently, the proposed method better incorporates information between any two labels. Behind this new framework any classification technique can be used as the base for training/predicting two-class sub-problems, but here we focus on using support vector machines (SVM). Experiments on real-world sets show that the new approach is usually better than the binary approach.