A Classifier Evaluation for Payments’ Default Predictions in a Brazilian Retail Company

  • Strauss Carvalho Cunha
  • Emanuel Mineda Carneiro
  • Lineu Fernando Stege Mialaret
  • Luiz Alberto Vieira Dias
  • Adilson Marques da Cunha
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 738)

Abstract

This article presents an investigation about the performance of classification algorithms used for predicting payments’ default. Classifiers used for modelling the data set include: Logistic Regression; Naive-Bayes; Decision Trees; Support Vector Machine; k-Nearest Neighbors; Random Forests; and Artificial Neural Networks. These classifiers were applied to both balanced and original data using the Weka data mining tool. Results from experiments revealed that Logistics Regression and Naive Bayes classifiers had the best performance for the chosen data set.

Keywords

Data mining Classifier algorithms Area under curve Logistic regression 

Notes

Acknowledgements

The authors would like to thank: (1) the Brazilian Aeronautics Institute of Technology (ITA); (2) the Casimiro Montenegro Filho Foundation (FCMF); the Software Engineering Research Group (GPES) members; and the 2RP Net Enterprise for their infrastructure, data set, assistance, advice, and financial support for this work.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Strauss Carvalho Cunha
    • 1
  • Emanuel Mineda Carneiro
    • 2
  • Lineu Fernando Stege Mialaret
    • 3
  • Luiz Alberto Vieira Dias
    • 2
  • Adilson Marques da Cunha
    • 2
  1. 1.Brazilian Federal Service of Data Processing - SERPROJacareiBrazil
  2. 2.Computer Science DepartmentBrazilian Aeronautics Institute of Technology - ITASao Jose dos CamposBrazil
  3. 3.Federal Institute of Education, Science and Technology of Sao Paulo - IFSPJacareiBrazil

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