Comparative analysis of data sampling techniques for legal text classification in real-world scenarios

Abstract This article investigates the use of machine learning (ML) for classifying legal texts, focusing on the challenges posed by imbalanced class distributions in datasets. In Brazil, where legal processes are numerous and complex, ML could improve judicial efficiency and decision-making speed. However, skewed data distributions make it difficult for standard algorithms to perform well across all classes, often neglecting minority categories. To address this, imbalance learning strategies such as oversampling, undersampling, and hybrid methods are applied to balance data sets and improve classification accuracy. Using real legal data from São Paulo, this study evaluates these techniques’ effectiveness in binary classification, providing valuable insights into their applicability in legal settings.

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Publication Details

Journal
Logic Journal of IGPL
Published
2025-05-16
DOI
https://doi.org/10.1093/jigpal/jzaf044
Primary Topic
Imbalanced Data Classification Techniques
Type
article
Field-Weighted Citation Impact
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article

Comparative analysis of data sampling techniques for legal text classification in real-world scenarios

Alex Marino Gonçalves de Almeida, Adriano Rívolli, Lukas Fernando de Oliveira Silva, Fabíola S. F. Pereira et al.
Logic Journal of IGPL
Imbalanced Data Classification Techniques
article

Comparative analysis of data sampling techniques for legal text classification in real-world scenarios

Alex Marino Gonçalves de Almeida, Adriano Rívolli, Lukas Fernando de Oliveira Silva, Fabíola S. F. Pereira, Daniela L. Freire, Giliard A. de Godoi, André C. P. L. F. de Carvalho, Márcio de Souza Dias, Márcio P. Basgalupp
article en

Abstract

Abstract This article investigates the use of machine learning (ML) for classifying legal texts, focusing on the challenges posed by imbalanced class distributions in datasets. In Brazil, where legal processes are numerous and complex, ML could improve judicial efficiency and decision-making speed. However, skewed data distributions make it difficult for standard algorithms to perform well across all classes, often neglecting minority categories. To address this, imbalance learning strategies such as oversampling, undersampling, and hybrid methods are applied to balance data sets and improve classification accuracy. Using real legal data from São Paulo, this study evaluates these techniques’ effectiveness in binary classification, providing valuable insights into their applicability in legal settings.

Logic Journal of IGPLVol. 34(1)
Universidade Tecnológica Federal do Paraná (BR), Universidade de São Paulo (BR), Microsoft (Brazil) (BR), Hospital Universitário da Universidade de São Paulo (BR), Ourofino Saúde Animal (Brazil) (BR), Universidade Federal de Uberlândia (BR), Universidade Federal de São Paulo (BR)
Openalex Percentile: Top 79%
Imbalanced Data Classification Techniques
DOISourceOpenAlex
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Comparative analysis of data sampling techniques for legal text classification in real-world scenarios — Alex Marino Gonçalves de Almeida, Adriano Rívolli, et al. · Logic Journal of IGPL (2025) | TGRS Research Map | TGRS