Multi-Label Classification via Manipulating Labels
Huaping Guo, Ming Yu Fan · Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013) · 2013
Unlike traditional classification problem, multi-label learning task is to predict a label set with unknown size for an example.While the exponential number of possible label sets challenges the task of multi-label learning.Many approaches by manipulating labels have been proposed.In this paper, we propose a new method via manipulating labels for multi-Label Learning: adding a virtual label to the original label set, appending the label subset selected by mutual information for each pairwise labels to the original feature set, and finally learning a binary classifier for each pairwise labels.Extensive experiments show that, compared with advanced multi-label methods, the proposed method induces models with significantly better performance..