Ensemble Feature Ranking

Jong, Kees; Mary, Jérémie; Cornuéjols, Antoine; Marchiori, Elena; Sebag, Michèle

Document type :

Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes

Title :

Ensemble Feature Ranking

Author(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]

Conference title :

ECML-PKDD

City :

Pisa

Country :

Italie

Start date of the conference :

2004-09-20

Publication date :

2004-09

HAL domain(s) :

Informatique [cs]/Apprentissage [cs.LG]
Informatique [cs]/Intelligence artificielle [cs.AI]

English abstract : [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 ...
Show more >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.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

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Ensemble Feature Ranking

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