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On the joint-effect of class imbalance and overlap: a critical review

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Abstract

Current research on imbalanced data recognises that class imbalance is aggravated by other data intrinsic characteristics, among which class overlap stands out as one of the most harmful. The combination of these two problems creates a new and difficult scenario for classification tasks and has been discussed in several research works over the past two decades. In this paper, we argue that despite some insightful information can be derived from related research, the joint-effect of class overlap and imbalance is still not fully understood, and advocate for the need to move towards a unified view of the class overlap problem in imbalanced domains. To that end, we start by performing a thorough analysis of existing literature on the joint-effect of class imbalance and overlap, elaborating on important details left undiscussed on the original papers, namely the impact of data domains with different characteristics and the behaviour of classifiers with distinct learning biases. This leads to the hypothesis that class overlap comprises multiple representations, which are important to accurately measure and analyse in order to provide a full characterisation of the problem. Accordingly, we devise two novel taxonomies, one for class overlap measures and the other for class overlap-based approaches, both resonating with the distinct representations of class overlap identified. This paper therefore presents a global and unique view on the joint-effect of class imbalance and overlap, from precursor work to recent developments in the field. It meticulously discusses some concepts taken as implicit in previous research, explores new perspectives in light of the limitations found, and presents new ideas that will hopefully inspire researchers to move towards a unified view on the problem and the development of suitable strategies for imbalanced and overlapped domains.

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Notes

  1. The reader may find supporting information in the supplementary material online at https://student.dei.uc.pt/~miriams/pdf-files/AIR_2021_Appendix.pdf.

  2. The interested reader may find detailed information on the performance of each classifier in the supplementary material provided online at https://student.dei.uc.pt/~miriams/pdf-files/AIR_2021_Appendix.pdf.

  3. https://github.com/miriamspsantos/pycol.

  4. https://archive.ics.uci.edu.

  5. https://www.kaggle.com.

  6. http://keel.es.

  7. https://www.openml.org.

  8. https://github.com/miriamspsantos/datagenerator.

  9. https://github.com/miriamspsantos/open-source-imbalance-overlap.

  10. https://github.com/miriamspsantos/pycol.

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Acknowledgements

This work is funded by national funds through the FCT-Foundation for Science and Technology, I.P., within the scope of the project CISUC-UID/CEC/00326/2020 and by European Social Fund, through the Regional Operational Program Centro 2020. This work is also partially supported by Andalusian frontier regional project A-TIC-434-UGR20 and by the Spanish Ministry of Science and Technology under project PID2020-119478GB-I00 including European Regional Development Funds. This work was also partially funded by the project Safe Cities-Inovação para Construir Cidades Seguras, with the reference POCI-01-0247-FEDER-041435, co-funded by the European Regional Development Fund (ERDF), through the Operational Programme for Competitiveness and Internationalization (COMPETE 2020), under the PORTUGAL 2020 Partnership Agreement. The work is further supported by the FCT Research Grant SFRH/BD/138749/2018.

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Authors and Affiliations

  1. Department of Informatics Engineering, Centre for Informatics and Systems of the University of Coimbra, University of Coimbra, Coimbra, Portugal

    Miriam Seoane Santos & Pedro Henriques Abreu

  2. Department of Computer Science, American University, Washington, DC, 20016, USA

    Nathalie Japkowicz

  3. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

    Alberto Fernández

  4. Fraunhofer Portugal AICOS and LIACC, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal

    Carlos Soares

  5. Institute of Computing Science, Poznan University of Technology, Poznan, Poland

    Szymon Wilk

  6. IPO-Porto Research Centre (CI-IPOP), Porto, Portugal

    João Santos

  7. Instituto de Ciências Biomédicas Abel Salazar da Universidade do Porto, Porto, Portugal

    João Santos

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  1. Miriam Seoane Santos
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  2. Pedro Henriques Abreu
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  3. Nathalie Japkowicz
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  4. Alberto Fernández
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  5. Carlos Soares
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  6. Szymon Wilk
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  7. João Santos
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Contributions

MSS Conceptualisation, Methodology, Literature Search, Investigation, Formal Analysis, Writing—Original Draft, Writing—Review and Editing, Visualisation. PHA Conceptualisation, Validation, Writing—Review and Editing, Supervision. NJ Validation, Writing—Review and Editing. AF Validation, Writing—Review and Editing. CS Validation, Writing—Review and Editing. SW Validation, Writing—Review and Editing. JS Writing—Review and Editing.

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Correspondence to Miriam Seoane Santos.

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https://github.com/miriamspsantos/pycol.

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Santos, M.S., Abreu, P.H., Japkowicz, N. et al. On the joint-effect of class imbalance and overlap: a critical review. Artif Intell Rev 55, 6207–6275 (2022). https://doi.org/10.1007/s10462-022-10150-3

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