Feature ranking in changing environments where new features are introduced

Alexandra Degeest, Michel Verleysen, Benoît Frénay

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Feature selection and taking into account dynamic environments are two important aspects of modern data analysis and machine learning. In particular, performing feature selection on datasets where the latest instances contain more features than the initial ones is a problem that may be encountered in many application areas where new sensors are acquired. This paper proposes a method for incremental feature selection with rankings combining the information extracted before and after the introduction of new features, even when the number of instances that include these new features is small. Results on three real-world datasets show that using the ranking of features on the original, smaller-dimensional dataset improves the feature selection results performed on the new, larger-dimensional dataset.

langue originaleAnglais
titreProceedings of the International Joint Conference on Neural Networks
EditeurInstitute of Electrical and Electronics Engineers Inc.
Nombre de pages8
ISBN (imprimé)9781479919604
Les DOIs
Etat de la publicationPublié - 28 sept. 2015
EvénementInternational Joint Conference on Neural Networks, IJCNN 2015 - Killarney, Irlande
Durée: 12 juil. 201517 juil. 2015

Une conférence

Une conférenceInternational Joint Conference on Neural Networks, IJCNN 2015
La villeKillarney

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