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Fast multilabel feature selection via global relevance and redundancy optimization

Jia Zhang and Yidong Lin and Min Jiang and Shaozi Li and Yong Tang and Jinyi Long and Jian Weng and Kay Chen Tan

IEEE TNNLS 2022 · published

Links pending

Contribution

Selects multilabel features by jointly optimizing relevance and redundancy, fast enough for practice.

Abstract

Multi-label feature selection faces a fundamental trade-off: features relevant for one label may be irrelevant or noisy for others, and naive global ranking discards useful label-specific structure. This paper proposes a fast multilabel feature selection framework that jointly optimizes global relevance and redundancy through a global optimization objective, achieving state-of-the-art accuracy across diverse multi-label benchmarks while running orders of magnitude faster than competing methods.

Why This Paper Matters

A practical, fast drop-in replacement for multi-label feature selection that improves results on standard benchmarks. Useful for anyone working on multi-label classification, tag recommendation, or hierarchical text categorization.

BibTeX

@article{zhang2022fast,
  title={Fast multilabel feature selection via global relevance and redundancy optimization},
  author={Zhang, Jia and Lin, Yidong and Jiang, Min and Li, Shaozi and Tang, Yong and Long, Jinyi and Weng, Jian and Tan, Kay Chen},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  volume={35},
  number={4},
  pages={5721--5734},
  year={2022},
  publisher={IEEE},
}