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.
@article{arxiv.1710.10335,
title = {Similarity-based Multi-label Learning},
author = {Ryan A. Rossi and Nesreen K. Ahmed and Hoda Eldardiry and Rong Zhou},
journal= {arXiv preprint arXiv:1710.10335},
year = {2017}
}