Title :

Feature selection for high dimensional regression using local search and statistical criteria

Author(s) :

Hamon, Julie [Auteur]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Dhaenens, Clarisse [Auteur]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Even, Gaël [Auteur]
Gènes Diffusion [Douai]
Jacques, Julien [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Laboratoire Paul Painlevé [LPP]

Conference title :

International Conference on Metaheuristics and Nature Inspired Computing

City :

Port El-Kantaoui

Country :

Tunisie

Start date of the conference :

2012-10-27

Publication date :

2012-07-15

HAL domain(s) :

Informatique [cs]/Recherche opérationnelle [cs.RO]
Statistiques [stat]/Théorie [stat.TH]
Mathématiques [math]/Statistiques [math.ST]

English abstract : [en]

Genomic selection is a genetic evaluation of animals from their DNA, based on a huge number of markers covering the whole genome. It requires advanced approaches and in particular feature selection methods. Feature selection ...
Show more >Genomic selection is a genetic evaluation of animals from their DNA, based on a huge number of markers covering the whole genome. It requires advanced approaches and in particular feature selection methods. Feature selection is a combinatorial problem that may be addressed by combinatorial optimization methods. We propose to combine an iterated local search (ILS) with a statistical evaluation of a multivariate regression and we compared three criteria in order to analyse their impact on the performance of the local search.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL