A comparison of GE optimized neural networks and decision trees
Authors: 
Kristopher Hoover, Rachel Marceau, Tyndall Harris, David Reif, Alison Motsinger-ReifAuthors Info & Claims
Kristopher Hoover, Rachel Marceau, Tyndall Harris, David Reif, Alison Motsinger-ReifAuthors Info & ClaimsPages 611 - 614
Abstract
Grammatical evolution neural networks (GENN) is a commonly utilized method at identifying difficult to detect gene-gene and gene-environment interactions. It has been shown to be an effective tool in the prediction of common diseases using single nucleotide polymorphisms (SNPs). However, GENN lacks interpretability because it is a black box model. Therefore, grammatical evolution of decision trees (GEDT) is being considered as an alternative, as decision trees are easily interpretable for clinicians. Previously, the most effective parameters for GEDT and GENN were found using parameter sweeps. Since GEDT is much more intuitive and easy to understand, it becomes important to compare its predictive power to that of GENN. We show that it is not as effective as GENN at detecting disease causing polymorphisms especially in more difficult to detect models, but this power trade off may be worth it for interpretability.
References
[1]
Moore, J.H., The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Hum Hered, 2003. 56(1--3): p. 73--82.
[2]
Motsinger, A.A., M.D. Ritchie, and D.M. Reif, Novel methods for detecting epistasis in pharmacogenomics studies. Pharmacogenomics, 2007. 8(9): p. 1229--41.
[3]
Hoover, Kristopher, Rachel Marceau, Tyndall Harris, Nicholas Hardison, David Reif, and Alison Motsinger-Reif. "Optimization of Grammatical Evolution Decision Trees." GECCO '11: Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation. Dublin, Ireland. New York: ACM, 2011. 35--36.
[4]
O'Neill, M. and C. Ryan, Grammatical Evolution: Evolutionary automatic programming in an arbitrary language. 2003, Boston: Kluwer Academic Publishers
[5]
Hastie, T.J., R.J. Tibshirani, and J.H. Friedman, The elements of statistical learning. Springer Series in Statistics. 2001, Basel: Springer Verlag.
[6]
Miller, B.L.G., D.E., Genetic Algorithms, Tournament Selection and the Effects of Noise. Complex Systems, 1995. 9(3): p. 193--212.
[7]
Alpaydin, E., Introduction to Machine Learning. 2004, Cambridge, MA: MIT Press.
[8]
Motsinger-Reif, A.A., et al., Grammatical evolution decision trees for detecting gene-gene interactions. BioData Min, 2010. 3(1): p. 8.
[9]
Motsinger-Reif, A. A., Dudek, S. M., Hahn, L. W. and Ritchie, M. D. (2008), Comparison of approaches for machine-learning optimization of neural networks for detecting gene-gene interactions in genetic epidemiology. Genet. Epidemiol., 32: 325--340.
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- A comparison of GE optimized neural networks and decision trees
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July 2012
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Published: 07 July 2012
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GECCO '12
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GECCO '12: Genetic and Evolutionary Computation Conference
July 7 - 11, 2012
Pennsylvania, Philadelphia, USA
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