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

Nicholas E. Hardison; David M. Reif; Theresa J. Fanelli; Marylyn D. Ritchie; Scott M. Dudek; Alison A. Motsinger-Reif

doi:10.1145/1389095.1389159

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

Nicholas E. Hardison, David M. Reif, Theresa J. Fanelli, Marylyn D. Ritchie, Scott M. Dudek, Alison A. Motsinger-Reif

Biochemistry & Molecular Biology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

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 resampling. 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.

Original language	English (US)
Title of host publication	GECCO'08
Subtitle of host publication	Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008
Publisher	Association for Computing Machinery
Pages	353-354
Number of pages	2
ISBN (Print)	9781605581309
DOIs	https://doi.org/10.1145/1389095.1389159
State	Published - 2008
Event	10th Annual Genetic and Evolutionary Computation Conference, GECCO 2008 - Atlanta, GA, United States Duration: Jul 12 2008 → Jul 16 2008

Publication series

Name	GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008

Other

Other	10th Annual Genetic and Evolutionary Computation Conference, GECCO 2008
Country/Territory	United States
City	Atlanta, GA
Period	7/12/08 → 7/16/08

All Science Journal Classification (ASJC) codes

Computational Theory and Mathematics
Computer Science Applications
Software

Access to Document

10.1145/1389095.1389159

Cite this

Hardison, N. E., Reif, D. M., Fanelli, T. J., Ritchie, M. D., Dudek, S. M., & Motsinger-Reif, A. A. (2008). A balanced accuracy fitness function leads to robust analysis using grammatical evolution neural networks in the case of class imbalance. In GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008 (pp. 353-354). (GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008). Association for Computing Machinery. https://doi.org/10.1145/1389095.1389159

Hardison, Nicholas E. ; Reif, David M. ; Fanelli, Theresa J. et al. / A balanced accuracy fitness function leads to robust analysis using grammatical evolution neural networks in the case of class imbalance. GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008. Association for Computing Machinery, 2008. pp. 353-354 (GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008).

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

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 resampling. 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.",

author = "Hardison, {Nicholas E.} and Reif, {David M.} and Fanelli, {Theresa J.} and Ritchie, {Marylyn D.} and Dudek, {Scott M.} and Motsinger-Reif, {Alison A.}",

year = "2008",

doi = "10.1145/1389095.1389159",

language = "English (US)",

isbn = "9781605581309",

series = "GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008",

publisher = "Association for Computing Machinery",

pages = "353--354",

booktitle = "GECCO'08",

note = "10th Annual Genetic and Evolutionary Computation Conference, GECCO 2008 ; Conference date: 12-07-2008 Through 16-07-2008",

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Hardison, NE, Reif, DM, Fanelli, TJ, Ritchie, MD, Dudek, SM & Motsinger-Reif, AA 2008, A balanced accuracy fitness function leads to robust analysis using grammatical evolution neural networks in the case of class imbalance. in GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008. GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008, Association for Computing Machinery, pp. 353-354, 10th Annual Genetic and Evolutionary Computation Conference, GECCO 2008, Atlanta, GA, United States, 7/12/08. https://doi.org/10.1145/1389095.1389159

A balanced accuracy fitness function leads to robust analysis using grammatical evolution neural networks in the case of class imbalance. / Hardison, Nicholas E.; Reif, David M.; Fanelli, Theresa J. et al.
GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008. Association for Computing Machinery, 2008. p. 353-354 (GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - 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 resampling. 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.

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Hardison NE, Reif DM, Fanelli TJ, Ritchie MD, Dudek SM, Motsinger-Reif AA. A balanced accuracy fitness function leads to robust analysis using grammatical evolution neural networks in the case of class imbalance. In GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008. Association for Computing Machinery. 2008. p. 353-354. (GECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008). doi: 10.1145/1389095.1389159

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

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