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

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@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

Citations