# Similarity-based Multi-label Learning

arXiv: https://arxiv.org/abs/1710.10335 · HTML: https://mlsanity.com/paper/1710.10335/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2017.

## Built on by (most cited first, 2 of 2)

- [A Similarity-based Framework for Classification Task](https://mlsanity.com/paper/2203.02669/) (2022)
- [Semantic F1 Scores: Fair Evaluation Under Fuzzy Class Boundaries](https://mlsanity.com/paper/2509.21633/) (2025)

## Abstract (arXiv)

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.