Title: Similarity-based Multi-label Learning,
Abstract: 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.