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2017 | OriginalPaper | Chapter

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

Authors : André Luiz Farias Novaes, Ricardo Tanscheit, Douglas Mota Dias

Published in: Progress in Artificial Intelligence

Publisher: Springer International Publishing

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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.

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Metadata
Title
Econometric Genetic Programming in Binary Classification: Evolving Logistic Regressions Through Genetic Programming
Authors
André Luiz Farias Novaes
Ricardo Tanscheit
Douglas Mota Dias
Copyright Year
2017
Book
Progress in Artificial Intelligence

Print ISBN: 978-3-319-65339-6

Electronic ISBN: 978-3-319-65340-2

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-65340-2_32

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