Machine Learning Algorithms Applied on Classification of Processes for Conciliation on Brazilian Labour Judiciary

• Filipe M. C. Barros Universidade Federal do Pará
• Cleison D. Silva Universidade Federal do Pará
• Igor R. M. Silva Universidade Federal do Rio Grande do Norte
• Victor S. Martins Universidade Federal do Pará
• Antonio J. S. Araújo Universidade Federal do Pará

DOI: https://doi.org/10.5753/eniac.2023.234189

Resumo

The Labour Judiciary ensures protection and justice in labour relations, resolving conflicts such as unfair dismissals and wage delays. Artificial intelligence emerges to expedite legal activities, assisting in dealing with the increasing case load in the Judiciary over the past years. In labor dispute resolution, conciliation is a recommended solution, offering speed and cost reduction. In this sense, this study proposes to evaluate models to predict the success of labor cases being resolved through conciliation. The dataset used to generate the models considered in this study consists of initial petitions from cases extracted from the Processo Judiciário Eletrônico (PJe) maintained by the Tribunal Regional do Trabalho da 8ª Região. Pre-processing steps were performed on these documents, including the removal of accents, special characters, numerals, punctuation, stopwords, conversion of text to lowercase, stemming, and tokenization. The next step was text vectorization using the Term Frequency-Inverse Document Frequency (TF-IDF) for model generation. For our analysis, three machine learning algorithms were taken into account: Support Vector Machines (SVM), logistic regression, and decision trees. Additionally, a boosted tree model (XGBoost) was also generated. Based on the analysis conducted, the SVM with RBF kernel yielded better results, achieving an accuracy of 83% and an F1-Score of 82%, with a Matthews Correlation Coefficient (MCC) of 0.66 and an Area Under the ROC Curve (AUC) of 0.83.

Palavras-chave: Labour Justice, Conciliation, Term Frequency-Inverse Document Frequency, Support Vector Machines, Logistic Regression, Decision Tree

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