Sélection de variables en apprentissage d'ordonnancement. évaluation des SVM pondérés

Lea Laporte, Sébastien Déjean, Josianne Mothe
2015 Document Numérique  
OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 15268 Any correspondence concerning this service should be sent to the repository administrator: staff-oatao@listes-diff.inp-toulouse.fr ABSTRACT. To select the most useful and the least redundant features to be used in ranking function to reduce computational
more » ... ts is an issue in learning to rank (LTR). Regularized SVM are promising approaches in this context. In this paper, we propose new feature selection algorithms for LTR based on weighted SVM. We investigate an ℓ2-AROM algorithm to solve the ℓ0 norm problem and a weighted ℓ2 algorithm to solve ℓ0 et ℓ1 norm problems. Experiments on benchmarks and commercial datasets show that our algorithms are up to 10 times faster and use up to 7 times less features than state-of-the-art methods, with similar ranking performance. MOTS-CLÉS : apprentissage d'ordonnancement, sélection de variables, SVM pondérés.
doi:10.3166/dn.18.1.97-121 fatcat:ju6xfabw5jflhmcb2jnrwwjl4i