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research-article

Learning label-specific features with global and local label correlation for multi-label classification

Authors:

Yuwen LiAuthors Info & Claims

Applied Intelligence, Volume 53, Issue 3

Pages 3017 - 3033

https://doi.org/10.1007/s10489-022-03386-7

Published: 19 May 2022 Publication History

Abstract

Multi-label algorithms often use an identical feature space to build classification models for all labels. However, labels generally express different semantic information and should have their own characteristics. A few algorithms have been proposed to find label-specific features to construct discriminative classification models. Some use global label correlation to make the reconstructed features more discriminative, but they usually neglect the local correlation between labels. To solve this problem, we propose a new algorithm, named learning Label-specific Features with Global and Local label Correlation (LFGLC). The algorithm integrates both global and local label correlation to extract label-specific features for each label. Specifically, global label correlation is calculated by the label co-occurrence frequency between label pairs, and local label correlation is learned from the neighborhood of each instance. Comprehensive experiments on 12 multi-label data sets clearly manifest that the proposed algorithm performs competitively in feature selection and multi-label classification.

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Cited By

Wang ZChen HMi YLuo CHorng SLi T(2024)Joint subspace reconstruction and label correlation for multi-label feature selectionApplied Intelligence10.1007/s10489-023-05188-x54:1(1117-1143)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s10489-023-05188-x
Zhu XRen JWang JLi J(2023)Automated machine learning with dynamic ensemble selectionApplied Intelligence10.1007/s10489-023-04770-753:20(23596-23612)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1007/s10489-023-04770-7

Index Terms

Learning label-specific features with global and local label correlation for multi-label classification
1. Computing methodologies
  1. Machine learning

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Applied Intelligence

Applied Intelligence Volume 53, Issue 3

Feb 2023

1168 pages

ISSN:0924-669X

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© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 19 May 2022

Accepted: 12 February 2022

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Natural Science Foundation of Fujian Province

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Cited By

Wang ZChen HMi YLuo CHorng SLi T(2024)Joint subspace reconstruction and label correlation for multi-label feature selectionApplied Intelligence10.1007/s10489-023-05188-x54:1(1117-1143)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s10489-023-05188-x
Zhu XRen JWang JLi J(2023)Automated machine learning with dynamic ensemble selectionApplied Intelligence10.1007/s10489-023-04770-753:20(23596-23612)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1007/s10489-023-04770-7

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