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Group-preserving label-specific feature selection for multi-label learning

Jia Zhang and Hanrui Wu and Min Jiang and Jinghua Liu and Shaozi Li and Yong Tang and Jinyi Long

ESWA 2023 · published

Links pending

Contribution

Preserves label-group structure while selecting label-specific features.

Abstract

In multi-label learning, features that matter for a group of related labels are often systematically different from features that matter for an isolated label. This paper introduces group-preserving label-specific feature selection, which identifies label clusters and selects features jointly tailored to each group while preserving the cluster structure. Experiments across multi-label benchmarks show consistent gains over both global and label-specific baselines, particularly when label relationships are structured rather than independent.

Why This Paper Matters

Targets the realistic case where labels come in groups (genres, disease categories, taxonomic levels) rather than as independent binary targets — directly useful for hierarchical and structured multi-label applications.

BibTeX

@article{zhang2023group,
  title={Group-preserving label-specific feature selection for multi-label learning},
  author={Zhang, Jia and Wu, Hanrui and Jiang, Min and Liu, Jinghua and Li, Shaozi and Tang, Yong and Long, Jinyi},
  journal={Expert Systems with Applications},
  volume={213},
  pages={118861},
  year={2023},
  publisher={Elsevier},
}