Use of classification techniques in a dataset on financial inclusion: a study based on Latin American countries

Authors

  • Pâmela Rodrigues Venturini de Souza

    Universidade Tecnológica Federal do Paraná - Campus Londrina

    Author

  • Bruno Gigioli Tomazi

    Universidade Tecnológica Federal do Paraná - Campus Londrina

    Author

  • Bruno Samways dos Santos

    Universidade Tecnológica Federal do Paraná - Campus Londrina

    Author

DOI:
https://doi.org/10.47456/bjpe.v8i1.37019
Keywords:
Data mining, Classification, Financial inclusion, Latin America
Abstract

A inclusão financeira é importante para reduzir a pobreza e proporcionar um crescimento econômico inclusivo, principalmente comparando grupos com grande desigualdade social. Este artigo utilizou a pesquisa Global Financial Inclusion (Global Findex) da World Bank Group para comparar técnicas de aprendizado de máquina na classificação de homens e mulheres quanto ao uso de serviços financeiros. Para isso, utilizou-se os classificadores Árvore de decisão, -vizinhos mais próximos, Naïve Bayes e Floresta randômica, e avaliadas as métricas de acurácia, precisão, sensibilidade, f1-score e área sob a curva Receiver Operating Characteristic (ROC). Verificou-se que todas as técnicas (exceto por Naïve Bayes) obtiveram uma acurácia próxima a 70%, sensibilidade próxima a 88% e precisão acima dos 72% na maioria dos parâmetros investigados. Quanto à área sob a curva ROC, a Floresta randômica atingiu 0,77, superando as outras técnicas nesta avaliação.

Author Biographies
  1. Pâmela Rodrigues Venturini de Souza, Universidade Tecnológica Federal do Paraná - Campus Londrina

    He has high school-secondary education at the FEDERAL INSTITUTE OF EDUCATION, SCIENCE AND TECHNOLOGY OF PARANA (2016). She is currently an Intern at Sonoco do Brasil. He has experience in the field of Production Engineering. (Text automatically generated by the CVLattes application)

  2. Bruno Gigioli Tomazi, Universidade Tecnológica Federal do Paraná - Campus Londrina

    Graduate in Production Engineering

  3. Bruno Samways dos Santos, Universidade Tecnológica Federal do Paraná - Campus Londrina

    Graduated in Production Engineering with emphasis on Control and Automation from the Federal Technological University of Paraná, Campus Ponta Grossa (UTFPR-PG), Master in Production Engineering from the same university and PhD in Production and Systems Engineering from the Pontifical Catholic University of Paraná ( PUCPR). He is a full professor at the Department of Production Engineering at the Universidade Tecnológica Federal do Paraná do Campus Londrina (UTFPR - LD) and holds the position of Substitute Coordinator of the Production Engineering Course. He is a lead researcher at the Data Mining and Optimization Research Group (GPOMD), working in the field of data mining research, with an emphasis on classification and clustering problems in several areas. He is interested in optimization models, machine learning techniques for various applications, such as education, public health, services and industries. (Text provided by the author)

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Published
2022-02-14
Issue
Vol. 8 No. 1 (2022): Número Regular (Janeiro - Março)
Section
OPERATIONAL RESEARCH
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Copyright (c) 2022 Pâmela Rodrigues Venturini de Souza, Bruno Gigioli Tomazi, Bruno Samways dos Santos (Autor)

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How to Cite

Souza, P. R. V. de, Tomazi, B. G., & Santos, B. S. dos. (2022). Use of classification techniques in a dataset on financial inclusion: a study based on Latin American countries. Brazilian Journal of Production Engineering, 8(1), 73-91. https://doi.org/10.47456/bjpe.v8i1.37019