Similarity-based Multi-label Learning

27 Oct 2017  ·  Ryan A. Rossi, Nesreen K. Ahmed, Hoda Eldardiry, Rong Zhou ·

Multi-label classification is an important learning problem with many applications. In this work, we propose a principled similarity-based approach for multi-label learning called SML. We also introduce a similarity-based approach for predicting the label set size. The experimental results demonstrate the effectiveness of SML for multi-label classification where it is shown to compare favorably with a wide variety of existing algorithms across a range of evaluation criterion.

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