TY - GEN
T1 - Econometric genetic programming in binary classification
T2 - 18th EPIA Conference on Artificial Intelligence, EPIA 2017
AU - Novaes, André Luiz Farias
AU - Tanscheit, Ricardo
AU - Dias, Douglas Mota
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Binary classification
KW - Genetic programming
KW - Logistic regression
KW - Model selection
UR - https://www.scopus.com/pages/publications/85028984549
U2 - 10.1007/978-3-319-65340-2_32
DO - 10.1007/978-3-319-65340-2_32
M3 - Conference contribution
AN - SCOPUS:85028984549
SN - 9783319653396
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 382
EP - 394
BT - Progress in Artificial Intelligence - 18th EPIA Conference on Artificial Intelligence, EPIA 2017, Proceedings
A2 - Vale, Zita
A2 - Oliveira, Eugenio
A2 - Gama, Joao
A2 - Lopes Cardoso, Henrique
PB - Springer Verlag
Y2 - 5 September 2017 through 8 September 2017
ER -