A balanced accuracy fitness function leads to robust analysis using grammatical evolution neural networks in the case of class imbalance

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Hardison:2008:gecco,

• author = "Nicholas E. Hardison and Theresa J. Fanelli and Scott M. Dudek and David M. Reif and Marylyn D. Ritchie and Alison A. Motsinger-Reif",
• title = "A balanced accuracy fitness function leads to robust analysis using grammatical evolution neural networks in the case of class imbalance",
• booktitle = "GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation",
• year = "2008",
• editor = "Maarten Keijzer and Giuliano Antoniol and Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and Nikolaus Hansen and John H. Holmes and Gregory S. Hornby and Daniel Howard and James Kennedy and Sanjeev Kumar and Fernando G. Lobo and Julian Francis Miller and Jason Moore and Frank Neumann and Martin Pelikan and Jordan Pollack and Kumara Sastry and Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and Ingo Wegener",
• isbn13 = "978-1-60558-130-9",
• pages = "353--354",
• address = "Atlanta, GA, USA",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• month = "12-16 " # jul,
• keywords = "genetic algorithms, genetic programming, grammatical evolution, gene-gene interactions, ANN, neural networks, SNP, single nucleotide polymorphism, Bioinformatics, computational biology: Poster",
• URL = "http://gpbib.cs.ucl.ac.uk/gecco2008/docs/p353.pdf",
• DOI = "doi:10.1145/1389095.1389159",
• size = "2 pages",
• abstract = "Grammatical Evolution Neural Networks (GENN) is a computational method designed to detect gene-gene interactions in genetic epidemiology, but has so far only been evaluated in situations with balanced numbers of cases and controls. Real data, however, rarely has such perfectly balanced classes. In the current study, we test the power of GENN to detect interactions in data with a range of class imbalance using two fitness functions (classification error and balanced error), as well as data re-sampling. We show that when using classification error, class imbalance greatly decreases the power of GENN. Re-sampling methods demonstrated improved power, but using balanced accuracy resulted in the highest power. Based on the results of this study, balanced error has replaced classification error in the GENN algorithm.",
• notes = "GECCO-2008 A joint meeting of the seventeenth international conference on genetic algorithms (ICGA-2008) and the thirteenth annual genetic programming conference (GP-2008).

  ACM Order Number 910081. Also known as \cite{1389159}",

}

Genetic Programming entries for Nicholas E Hardison Theresa J Fanelli Scott M Dudek David M Reif Marylyn D Ritchie Alison A Motsinger

Citations