Techniques for Dealing with Imbalanced Data: A Systematic Literature Review

• Leandro O. da Silva USP
• Daniela L. Freire USP
• Márcio P. Basgalupp USP
• André C. P. L. F. de Carvalho USP

DOI: https://doi.org/10.5753/stil.2025.37843

Resumo

This systematic review of the literature addresses techniques employed to address the problem of data imbalance. A variety of articles were analyzed, exploring strategies such as under-sampling, oversampling, and their combinations to address asymmetry in class distributions. Sensitive metrics, including recall, precision, and F1 score, emerge as crucial in imbalanced contexts. The studies reveal the challenges in selecting appropriate strategies and underscore the importance of adaptive approaches. Innovative solutions, such as adaptive combinations of techniques and integration with specific algorithms, are discussed. The ongoing need for research to address the specific challenges of data imbalance is highlighted.

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