Using random walks for multi-label classification

The Multi-Label Classification (MLC) problem has aroused wide concern in these years since the multi-labeled data appears in many applications, such as page categorization, tag recommendation, mining of semantic web data, social network analysis, and so forth. In this paper, we propose a novel MLC solution based on the random walk model, called MLRW. MLRW maps the multi-labeled instances to graphs, on which the random walk is applied. When an unlabeled data is fed, MLRW transforms the original multi-label problem to some single-label subproblems. Experimental results on several real-world data sets demonstrate that MLRW is a better solution to the MLC problems than many other existing multi-label classification methods.