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Imbalanced data preprocessing techniques for machine learning: a systematic mapping study

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Abstract

Machine Learning (ML) algorithms have been increasingly replacing people in several application domains—in which the majority suffer from data imbalance. In order to solve this problem, published studies implement data preprocessing techniques, cost-sensitive and ensemble learning. These solutions reduce the naturally occurring bias towards the majority sample through ML. This study uses a systematic mapping methodology to assess 9927 papers related to sampling techniques for ML in imbalanced data applications from 7 digital libraries. A filtering process selected 35 representative papers from various domains, such as health, finance, and engineering. As a result of a thorough quantitative analysis of these papers, this study proposes two taxonomies—illustrating sampling techniques and ML models. The results indicate that oversampling and classical ML are the most common preprocessing techniques and models, respectively. However, solutions with neural networks and ensemble ML models have the best performance—with potentially better results through hybrid sampling techniques. Finally, none of the 35 works apply simulation-based synthetic oversampling, indicating a path for future preprocessing solutions.

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Acknowledgements

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001, and by the National Council for Scientific and Technological Development (CNPq). We would also like to thank the University of Vale do Rio dos Sinos (Unisinos).

Funding

This study was financed in part by the following Brazilian federal organizations. Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)—Finance Code 001; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)—Award Number 306395/2017-7.

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  1. Applied Computing Graduate Program, University of Vale do Rio dos Sinos, São Leopoldo, Rio Grande do Sul, 93022-750, Brazil

    Vitor Werner de Vargas, Jorge Arthur Schneider Aranda & Jorge Luis Victória Barbosa

  2. Electrical Engineering Graduate Program, University of Vale do Rio dos Sinos, São Leopoldo, Rio Grande do Sul, 93022-750, Brazil

    Ricardo dos Santos Costa, Paulo Ricardo da Silva Pereira & Jorge Luis Victória Barbosa

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All authors contributed to the study, read and approved the final manuscript. The list below describes the CRediT (Contributor Roles Taxonomy) by author: VWV: Conceptualization, Methodology, Formal analysis, Writing—Original Draft; JASA: Writing—Review and Editing; RSC: Writing—Review and Editing; PRSP: Writing—Review and Editing, Supervision; JLVB: Writing—Review and Editing, Supervision.

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Werner de Vargas, V., Schneider Aranda, J.A., dos Santos Costa, R. et al. Imbalanced data preprocessing techniques for machine learning: a systematic mapping study. Knowl Inf Syst 65, 31–57 (2023). https://doi.org/10.1007/s10115-022-01772-8

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