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

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Nicholas E. Hardison,

Alison A. Motsinger-ReifAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 353 - 354

https://doi.org/10.1145/1389095.1389159

Published: 12 July 2008 Publication History

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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

References

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Sri-iesaranusorn PChaiyaroj ABuekban CDumnin SPongthornseri RThanawattano CSurangsrirat D(2021)Classification of 41 Hand and Wrist Movements via Surface Electromyogram Using Deep Neural NetworkFrontiers in Bioengineering and Biotechnology10.3389/fbioe.2021.5483579Online publication date: 9-Jun-2021
https://doi.org/10.3389/fbioe.2021.548357

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

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GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

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Association for Computing Machinery

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Published: 12 July 2008

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GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

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Sri-iesaranusorn PChaiyaroj ABuekban CDumnin SPongthornseri RThanawattano CSurangsrirat D(2021)Classification of 41 Hand and Wrist Movements via Surface Electromyogram Using Deep Neural NetworkFrontiers in Bioengineering and Biotechnology10.3389/fbioe.2021.5483579Online publication date: 9-Jun-2021
https://doi.org/10.3389/fbioe.2021.548357

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The power of quantitative grammatical evolution neural networks to detect gene-gene interactions

Genetic programming neural networks: A powerful bioinformatics tool for human genetics

Grammatical evolution of neural networks for discovering epistasis among quantitative trait loci

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