Genetic Programming for Classification with Unbalanced Data

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Bhowan:2010:EuroGP,

• author = "Urvesh Bhowan and Mengjie Zhang and Mark Johnston",
• title = "Genetic Programming for Classification with Unbalanced Data",
• booktitle = "Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010",
• year = "2010",
• editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and Sara Silva and Stephen Dignum and A. Sima Uyar",
• volume = "6021",
• series = "LNCS",
• pages = "1--13",
• address = "Istanbul",
• month = "7-9 " # apr,
• organisation = "EvoStar",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-642-12147-0",
• DOI = "doi:10.1007/978-3-642-12148-7_1",
• abstract = "Learning algorithms can suffer a performance bias when data sets only have a small number of training examples for one or more classes. In this scenario learning methods can produce the deceptive appearance of good looking results even when classification performance on the important minority class can be poor. This paper compares two Genetic Programming (GP) approaches for classification with unbalanced data. The first focuses on adapting the fitness function to evolve classifiers with good classification ability across both minority and majority classes. The second uses a multi-objective approach to simultaneously evolve a Pareto front (or set) of classifiers along the minority and majority class trade-off surface. Our results show that solutions with good classification ability were evolved across a range of binary classification tasks with unbalanced data.",
• notes = "Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010 held in conjunction with EvoCOP2010 EvoBIO2010 and EvoApplications2010",

}

Genetic Programming entries for Urvesh Bhowan Mengjie Zhang Mark Johnston

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