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Feature selection in high dimensional regression problems for genomic

Julie Hamon 1, 2 Clarisse Dhaenens 2, 1 Gaël Even 3 Julien Jacques 4, 5 
1 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
4 MODAL - MOdel for Data Analysis and Learning
LPP - Laboratoire Paul Painlevé - UMR 8524, Université de Lille, Sciences et Technologies, Inria Lille - Nord Europe, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille
Abstract : In the context of genomic selection in animal breeding, an important objective consists in looking for explicative markers for a phe- notype under study. In order to deal with a high number of markers, we propose to use combinatorial optimization to perform variable selection. Results show that our approach outperforms some classical and widely used methods on simulated and "closed to real" datasets.
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Submitted on : Saturday, June 29, 2013 - 8:04:01 AM
Last modification on : Tuesday, November 22, 2022 - 2:26:13 PM
Long-term archiving on: : Monday, September 30, 2013 - 4:06:10 AM


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  • HAL Id : hal-00839705, version 1


Julie Hamon, Clarisse Dhaenens, Gaël Even, Julien Jacques. Feature selection in high dimensional regression problems for genomic. Tenth International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, Jun 2013, Nice, France. ⟨hal-00839705⟩



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