On the Impact of the Objective Function on Imbalanced Data using Cartesian Genetic Programming Neuroevolutionary Approaches

Created by W.Langdon from gp-bibliography.bib Revision:1.7954

@InProceedings{Melo-Neto:2019:CEC,

• author = "Johnathan M. {Melo Neto} and Heder S. Bernardino and Helio J. C. Barbosa",
• title = "On the Impact of the Objective Function on Imbalanced Data using Cartesian Genetic Programming Neuroevolutionary Approaches",
• booktitle = "2019 IEEE Congress on Evolutionary Computation (CEC)",
• year = "2019",
• pages = "1860--1867",
• month = jun,
• keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming, ANN",
• DOI = "doi:10.1109/CEC.2019.8789947",
• abstract = "The training of machine learning models for imbalanced data classification is a challenging task. Several metrics have been used to assess the performance of the classifiers. Each metric is appropriate for a class of problems, and some users often do not have a clear notion of which metric to use. In such cases, it is desirable that the chosen objective function provides good overall performance for most of the existing metrics. Here, three neuroevolutionary approaches based on Cartesian Genetic Programming are used in order to investigate the impact of optimizing accuracy, G-mean, Fbeta-score, and the area under the Receiver Operating Characteristic (ROC) curve when creating classifiers based on Artificial Neural Networks applied to imbalanced data classification problems. The results suggest that the optimization of G-mean and FB-score generate models that present a superior overall performance in all metrics.",
• notes = "Also known as \cite{8789947}",

}

Genetic Programming entries for Johnathan Mayke Melo Neto Heder Soares Bernardino Helio J C Barbosa

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