About Filter Criteria for Feature Selection in Regression

Alexandra Degeest, Michel Verleysen, Benoît Frénay

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Résumé

Selecting the best group of features from high-dimensional datasets is an important challenge in machine learning. Indeed problems with hundreds of features have now become usual. In the context of filter methods, the selected relevance criterion used for filtering is the key factor of a feature selection method. To select an appropriate criterion among the numerous existing ones, this paper proposes a list of six necessary properties. This paper describes then three relevance criteria, the mutual information, the noise variance and the adjusted R-squared, and compares them in the view of the aforementioned properties. Any new, or popular, criterion could be analysed in the light of these properties.

langue originaleAnglais
titreAdvances in Computational Intelligence - 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Proceedings
rédacteurs en chefIgnacio Rojas, Gonzalo Joya, Andreu Catala
EditeurSpringer Verlag
Pages579-590
Nombre de pages12
ISBN (imprimé)9783030205171
Les DOIs
Etat de la publicationPublié - 1 janv. 2019
Evénement15th International Work-Conference on Artificial Neural Networks, IWANN 2019 - Gran Canaria, Espagne
Durée: 12 juin 201914 juin 2019

Série de publications

NomLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11507 LNCS
ISSN (imprimé)0302-9743
ISSN (Electronique)1611-3349

Une conférence

Une conférence15th International Work-Conference on Artificial Neural Networks, IWANN 2019
PaysEspagne
La villeGran Canaria
période12/06/1914/06/19

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    Degeest, A., Verleysen, M., & Frénay, B. (2019). About Filter Criteria for Feature Selection in Regression. Dans I. Rojas, G. Joya, & A. Catala (eds.), Advances in Computational Intelligence - 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Proceedings (p. 579-590). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol 11507 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-20518-8_48