Improving Fitness Functions in Genetic Programming for Classification on Unbalanced Credit Card Data

Created by W.Langdon from gp-bibliography.bib Revision:1.7662

@InProceedings{conf/evoW/CaoLONM16,

• author = "Van Loi Cao and Nhien-An Le-Khac and Michael O'Neill and Miguel Nicolau and James McDermott",
• title = "Improving Fitness Functions in Genetic Programming for Classification on Unbalanced Credit Card Data",
• booktitle = "19th European Conference on Applications of Evolutionary Computation, EvoApplications 2016",
• year = "2016",
• editor = "Giovanni Squillero and Paolo Burelli",
• volume = "9597",
• series = "Lecture Notes in Computer Science",
• pages = "35--45",
• address = "Porto, Portugal",
• month = mar # " 30 -- " # apr # " 1",
• organisation = "EvoStar",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, Class imbalance, Credit card data, Fitness functions",
• bibdate = "2016-03-23",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/evoW/evoappl2016-1.html#CaoLONM16",
• isbn13 = "978-3-319-31204-0",
• DOI = "doi:10.1007/978-3-319-31204-0_3",
• abstract = "Credit card classification based on machine learning has attracted considerable interest from the research community. One of the most important tasks in this area is the ability of classifiers to handle the imbalance in credit card data. In this scenario, classifiers tend to yield poor accuracy on the minority class despite realizing high overall accuracy. This is due to the influence of the majority class on traditional training criteria. In this paper, we aim to apply genetic programming to address this issue by adapting existing fitness functions. We examine two fitness functions from previous studies and develop two new fitness functions to evolve GP classifiers with superior accuracy on the minority class and overall. Two UCI credit card datasets are used to evaluate the effectiveness of the proposed fitness functions. The results demonstrate that the proposed fitness functions augment GP classifiers, encouraging fitter solutions on both the minority and the majority classes.",
• notes = "EvoApplications2016 held inconjunction with EuroGP'2016, EvoCOP2016 and EvoMUSART 2016",

}

Genetic Programming entries for Van Loi Cao Nhien-An Le-Khac Michael O'Neill Miguel Nicolau James McDermott

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