research-article

MULFE: Multi-Label Learning via Label-Specific Feature Space Ensemble

Authors:

Xindong WuAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 16, Issue 1

Article No.: 5, Pages 1 - 24

https://doi.org/10.1145/3451392

Published: 20 July 2021 Publication History

Abstract

In multi-label learning, label correlations commonly exist in the data. Such correlation not only provides useful information, but also imposes significant challenges for multi-label learning. Recently, label-specific feature embedding has been proposed to explore label-specific features from the training data, and uses feature highly customized to the multi-label set for learning. While such feature embedding methods have demonstrated good performance, the creation of the feature embedding space is only based on a single label, without considering label correlations in the data. In this article, we propose to combine multiple label-specific feature spaces, using label correlation, for multi-label learning. The proposed algorithm, multi-label-specific feature space ensemble (MULFE), takes consideration label-specific features, label correlation, and weighted ensemble principle to form a learning framework. By conducting clustering analysis on each label’s negative and positive instances, MULFE first creates features customized to each label. After that, MULFE utilizes the label correlation to optimize the margin distribution of the base classifiers which are induced by the related label-specific feature spaces. By combining multiple label-specific features, label correlation based weighting, and ensemble learning, MULFE achieves maximum margin multi-label classification goal through the underlying optimization framework. Empirical studies on 10 public data sets manifest the effectiveness of MULFE.

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Index Terms

MULFE: Multi-Label Learning via Label-Specific Feature Space Ensemble
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Structured outputs
    2. Learning settings
      1. Semi-supervised learning settings

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 16, Issue 1

February 2022

475 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3472794

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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Copyright © 2021 Association for Computing Machinery.

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Publication History

Published: 20 July 2021

Accepted: 01 February 2021

Revised: 01 January 2021

Received: 01 May 2020

Published in TKDD Volume 16, Issue 1

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https://doi.org/10.1109/TFUZZ.2024.3419144
Zhao TZhang YMiao D(2025)Granular correlation-based label-specific feature augmentation for multi-label classificationInformation Sciences10.1016/j.ins.2024.121473689(121473)Online publication date: Jan-2025
https://doi.org/10.1016/j.ins.2024.121473
Zhang YZhao TMiao DYao Y(2025)Three-way multi-label classification: A review, a framework, and new challengesApplied Soft Computing10.1016/j.asoc.2025.112757171(112757)Online publication date: Mar-2025
https://doi.org/10.1016/j.asoc.2025.112757
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