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Consensus Features Nested Cross-Validation

View ORCID ProfileSaeid Parvandeh, Hung-Wen Yeh, Martin P. Paulus, View ORCID ProfileBrett A. McKinney
doi: https://doi.org/10.1101/2019.12.31.891895
Saeid Parvandeh
1Tandy School of Computer Science, University of Tulsa, Tulsa, OK, United States
2Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, United States
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Hung-Wen Yeh
3Health Services and Outcomes Research, Children’s Mercy Hospital, Kansas City, MO, United States
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Martin P. Paulus
4Laureate Institute for Brain Research, Tulsa, OK, United States
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Brett A. McKinney
1Tandy School of Computer Science, University of Tulsa, Tulsa, OK, United States
5Department of Mathematics, University of Tulsa, Tulsa, OK, United States
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  • ORCID record for Brett A. McKinney
  • For correspondence: brett.mckinney@gmail.com
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Abstract

Motivation Feature selection can improve the accuracy of machine learning models, but appropriate steps must be taken to avoid overfitting. Nested cross-validation (nCV) is a common approach that chooses the classification model and features to represent a given outer fold based on features that give the maximum inner-fold accuracy. Differential privacy is a related technique to avoid overfitting that uses a privacy preserving noise mechanism to identify features that are stable between training and holdout sets.

Methods We develop consensus nested CV (cnCV) that combines the idea of feature stability from differential privacy with nested CV. Feature selection is applied in each inner fold and the consensus of top features across folds is a used as a measure of feature stability or reliability instead of classification accuracy, which is used in standard nCV. We use simulated data with main effects, correlation, and interactions to compare the classification accuracy and feature selection performance of the new cnCV with standard nCV, Elastic Net optimized by CV, differential privacy, and private Evaporative Cooling (pEC). We also compare these methods using real RNA-Seq data from a study of major depressive disorder.

Results The cnCV method has similar training and validation accuracy to nCV, but cnCV has much shorter run times because it does not construct classifiers in the inner folds. The cnCV method chooses a more parsimonious set of features with fewer false positives than nCV. The cnCV method has similar accuracy to pEC and cnCV selects stable features between folds without the need to specify a privacy threshold. We show that cnCV is an effective and efficient approach for combining feature selection with classification.

Availability Code available at https://github.com/insilico/cncv.

Contact brett.mckinney{at}utulsa.edu

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Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted January 02, 2020.
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Consensus Features Nested Cross-Validation
Saeid Parvandeh, Hung-Wen Yeh, Martin P. Paulus, Brett A. McKinney
bioRxiv 2019.12.31.891895; doi: https://doi.org/10.1101/2019.12.31.891895
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Consensus Features Nested Cross-Validation
Saeid Parvandeh, Hung-Wen Yeh, Martin P. Paulus, Brett A. McKinney
bioRxiv 2019.12.31.891895; doi: https://doi.org/10.1101/2019.12.31.891895

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