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A Survey on Ensemble Multi-label Classifiers

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Proceedings of the 14th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2022) (SoCPaR 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 648))

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Abstract

Ensemble approach for multi-label classification has received great attention from the Machine Learning community over the last decade. It has been developed on top of the problem transformation or algorithm adaptation methods to overcome some scientific challenges introduced by multi-label learning. The success of ensemble models in this classification paradigm arises by offering an appealing solution to different problems, such as label dependencies, class imbalance, and high dimensionality. In this study, we present a structured synthesis of different ensemble multi-label classifiers including their different taxonomies cited in the literature.

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Author information

Authors and Affiliations

  1. LRI Laboratory, Badji Mokhtar University, BP 12, 23000, Annaba, Algeria

    Sonia Guehria

  2. LABGED Laboratory, Badji Mokhtar University, BP 12, 23000, Annaba, Algeria

    Habiba Belleili & Nabiha Azizi

Authors
  1. Sonia Guehria
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  2. Habiba Belleili
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  3. Nabiha Azizi
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Corresponding author

Correspondence to Sonia Guehria .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Data Science, FLAME University, Pune, Maharashtra, India

    Ajith Abraham

  2. University of Applied Sciences and Arts Northwestern Switzerland, Olten, Switzerland

    Thomas Hanne

  3. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Auburn, AL, USA

    Niketa Gandhi

  4. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Mala, Kerala, India

    Pooja Manghirmalani Mishra

  5. Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Anu Bajaj

  6. Université Paris-Est Créteil, Créteil, France

    Patrick Siarry

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Guehria, S., Belleili, H., Azizi, N. (2023). A Survey on Ensemble Multi-label Classifiers. In: Abraham, A., Hanne, T., Gandhi, N., Manghirmalani Mishra, P., Bajaj, A., Siarry, P. (eds) Proceedings of the 14th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2022). SoCPaR 2022. Lecture Notes in Networks and Systems, vol 648. Springer, Cham. https://doi.org/10.1007/978-3-031-27524-1_11

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