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Econometric Genetic Programming in Binary Classification: Evolving Logistic Regressions Through Genetic Programming

  • André Luiz Farias NovaesEmail author
  • Ricardo Tanscheit
  • Douglas Mota Dias
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10423)

Abstract

Logistic Regression and Genetic Programming (GP) have already been compared to each other in classification tasks. In this paper, Econometric Genetic Programming (EGP), first introduced as a regression methodology, is extended to binary classification tasks and evolves logistic regressions through GP, aiming to generate high accuracy classifications with potential interpretability of parameters, while uses statistical significance as a feature-selection tool and GP for model selection. EGP-Classification (or EGP-C), the name of this proposed EGP’s extension, was tested against a large group of algorithms in three cross-sectional datasets, showing competitive results in most of them. EGP-C successfully competed against highly non-linear algorithms, like Support Vector Machines and Multilayer Perceptron with Back Propagation, and still allows interpretability of parameters and models generated.

Keywords

Genetic programming Binary classification Logistic regression Model selection 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • André Luiz Farias Novaes
    • 1
    Email author
  • Ricardo Tanscheit
    • 2
  • Douglas Mota Dias
    • 3
  1. 1.Informatics DepartmentUniversity of LisbonLisbonPortugal
  2. 2.Electrical Engineering DepartmentPUC-RioRio de JaneiroBrazil
  3. 3.Electronics and Telecommunications Engineering DepartmentUERJRio de JaneiroBrazil

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