Comparison Between Filter Criteria for Feature Selection in Regression

Alexandra Degeest, Michel Verleysen, Benoît Frénay

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Résumé

High-dimensional data are ubiquitous in regression. To obtain a better understanding of the data or to ease the learning process, reducing the data to a subset of the most relevant features is important. Among the different methods of feature selection, filter methods are popular because they are independent from the model, which makes them fast and computationally simpler than other feature selection methods. The key factor of a filter method is the filter criterion. This paper focuses on which properties make a good filter criterion, in order to be able to select one from the numerous existing ones. Six properties are discussed, and three filter criteria are compared with respect to the aforementioned properties.

langue originaleAnglais
titreArtificial Neural Networks and Machine Learning – ICANN 2019
Sous-titreDeep Learning - 28th International Conference on Artificial Neural Networks, Proceedings
rédacteurs en chefIgor V. Tetko, Pavel Karpov, Fabian Theis, Vera Kurková
EditeurSpringer Verlag
Pages59-71
Nombre de pages13
ISBN (imprimé)9783030304836
Les DOIs
Etat de la publicationPublié - 1 janv. 2019
Evénement28th International Conference on Artificial Neural Networks, ICANN 2019 - Munich, Allemagne
Durée: 17 sept. 201919 sept. 2019

Série de publications

NomLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11728 LNCS
ISSN (imprimé)0302-9743
ISSN (Electronique)1611-3349

Une conférence

Une conférence28th International Conference on Artificial Neural Networks, ICANN 2019
PaysAllemagne
La villeMunich
période17/09/1919/09/19

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  • Contient cette citation

    Degeest, A., Verleysen, M., & Frénay, B. (2019). Comparison Between Filter Criteria for Feature Selection in Regression. Dans I. V. Tetko, P. Karpov, F. Theis, & V. Kurková (eds.), Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning - 28th International Conference on Artificial Neural Networks, Proceedings (p. 59-71). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol 11728 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-30484-3_5