Avaliação empírica de classificadores e métodos de balanceamento para detecção de fraudes em transações com cartões de créditos

• Victor Nicola University of São Paulo
• Marcelo Lauretto EACH-USP
• Karina Valdivia Delgado Universidade de São Paulo

DOI: https://doi.org/10.5753/eniac.2020.12118

Resumo

Algoritmos de aprendizado de máquina são amplamente utilizados em sistemas para detecção de fraudes em cartões de crédito devido à capacidade de distinguir entre transações legítimas e fraudulentas. Um problema reconhecido nesta área é o alto desbalanceamento usualmente encontrado nas classes, que pode comprometer o desempenho dos classificadores. Os estudos empíricos encontrados na literatura aplicam, no máximo, duas técnicas de amostragem. Este artigo traz um estudo comparativo de cinco modelos de classificação sob cinco diferentes métodos de balanceamento dos conjuntos de treinamento. O melhor desempenho foi obtido pela random forest, que além de ter apresentado o maior F-score médio (0,867), mostrou-se consideravelmente mais robusta do que os demais classificadores em relação à escolha da técnica de balanceamento e à seleção de atributos.

Palavras-chave: Detecção de Fraudes, Cartão de Crédito, Balanceamento, Aprendizado Supervisionado, Random Forest

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