Learning Tree Structure of Label Dependency for Multi-label Learning

Springer Berlin / Heidelberg
Publication Type:
Conference Proceeding
Advances in Knowledge Discovery and Data Mining Lecture Notes in Computer Science, 2012, 7301 pp. 159 - 170
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There always exists some kind of label dependency in multi- label data. Learning and utilizing those dependencies could improve the learning performance further. Therefore, an approach for multi-label learning is proposed in this paper, which quantifies the dependencies of pairwise labels firstly, and then builds a tree structure of the labels to describe them. Thus the approach could find out potential strong la- bel dependencies and produce more generalized dependent relationships. The experimental results have validated that compared with other state- of-the-art algorithms, the method is not only a competitive alternative, but also has shown better performance after ensemble learning especially.
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