Using random walks for multi-label classification

Authors:
Chaokun Wang;Wei Zheng;Zhang Liu;Yiyuan Bai;Jianmin Wang
Affiliations:
Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China;Tsinghua University, Beijing, China
Venue:
Proceedings of the 20th ACM international conference on Information and knowledge management
Year:
2011

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Abstract

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.