Ensemble Feature Ranking
Type de document :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
Titre :
Ensemble Feature Ranking
Auteur(s) :
Jong, Kees [Auteur]
Mary, Jérémie [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Cornuéjols, Antoine [Auteur]
Mathématiques et Informatique Appliquées [MIA-Paris]
Marchiori, Elena [Auteur]
Sebag, Michèle [Auteur]
Laboratoire de Recherche en Informatique [LRI]
Mary, Jérémie [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Cornuéjols, Antoine [Auteur]
Mathématiques et Informatique Appliquées [MIA-Paris]
Marchiori, Elena [Auteur]
Sebag, Michèle [Auteur]
Laboratoire de Recherche en Informatique [LRI]
Titre de la manifestation scientifique :
ECML-PKDD
Ville :
Pisa
Pays :
Italie
Date de début de la manifestation scientifique :
2004-09-20
Date de publication :
2004-09
Discipline(s) HAL :
Informatique [cs]/Apprentissage [cs.LG]
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Intelligence artificielle [cs.AI]
Résumé en anglais : [en]
A crucial issue for Machine Learning and Data Mining is Feature Selection, selecting the relevant features in order to focus the learning search. A relaxed setting for Feature Selection is known as Feature Ranking, ranking ...
Lire la suite >A crucial issue for Machine Learning and Data Mining is Feature Selection, selecting the relevant features in order to focus the learning search. A relaxed setting for Feature Selection is known as Feature Ranking, ranking the features with respect to their relevance. This paper proposes an ensemble approach for Feature Ranking, aggre-gating feature rankings extracted along independent runs of an evolutionary learning algorithm named ROGER. The convergence of ensemble feature ranking is studied in a theoretical perspective, and a statistical model is devised for the empirical validation, inspired from the complexity framework proposed in the Constraint Satisfaction domain. Comparative experiments demonstrate the robustness of the approach for learning (a limited kind of) non-linear concepts, specifically when the features significantly outnumber the examples.Lire moins >
Lire la suite >A crucial issue for Machine Learning and Data Mining is Feature Selection, selecting the relevant features in order to focus the learning search. A relaxed setting for Feature Selection is known as Feature Ranking, ranking the features with respect to their relevance. This paper proposes an ensemble approach for Feature Ranking, aggre-gating feature rankings extracted along independent runs of an evolutionary learning algorithm named ROGER. The convergence of ensemble feature ranking is studied in a theoretical perspective, and a statistical model is devised for the empirical validation, inspired from the complexity framework proposed in the Constraint Satisfaction domain. Comparative experiments demonstrate the robustness of the approach for learning (a limited kind of) non-linear concepts, specifically when the features significantly outnumber the examples.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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