Smoothness bias in relevance estimators for feature selection in regression

Alexandra Degeest, Michel Verleysen, Benoît Frénay

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Résumé

Selecting features from high-dimensional datasets is an important problem in machine learning. This paper shows that in the context of filter methods for feature selection, the estimator of the criterion used to select features plays an important role; in particular the estimators may suffer from a bias when comparing smooth and non-smooth features. This paper analyses the origin of such bias and investigates whether this bias influences the results of the feature selection process. Results show that non-smooth features tend to be penalised especially in small datasets.

langue originaleAnglais
titreArtificial Intelligence Applications and Innovations - 14th IFIP WG 12.5 International Conference, AIAI 2018, Proceedings
rédacteurs en chefIlias Maglogiannis, Lazaros Iliadis, Vassilis Plagianakos
EditeurSpringer New York
Pages285-294
Nombre de pages10
ISBN (imprimé)9783319920061
Les DOIs
Etat de la publicationPublié - 1 janv. 2018
Evénement14th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2018 - Rhodes, Grèce
Durée: 25 mai 201827 mai 2018

Série de publications

NomIFIP Advances in Information and Communication Technology
Volume519
ISSN (imprimé)1868-4238

Une conférence

Une conférence14th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2018
PaysGrèce
La villeRhodes
période25/05/1827/05/18

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  • Contient cette citation

    Degeest, A., Verleysen, M., & Frénay, B. (2018). Smoothness bias in relevance estimators for feature selection in regression. Dans I. Maglogiannis, L. Iliadis, & V. Plagianakos (eds.), Artificial Intelligence Applications and Innovations - 14th IFIP WG 12.5 International Conference, AIAI 2018, Proceedings (p. 285-294). (IFIP Advances in Information and Communication Technology; Vol 519). Springer New York. https://doi.org/10.1007/978-3-319-92007-8_25