Comparison Between Filter Criteria for Feature Selection in Regression

Alexandra Degeest; Michel Verleysen; Benoît Frénay

doi:10.1007/978-3-030-30484-3_5

Comparison Between Filter Criteria for Feature Selection in Regression

Alexandra Degeest, Michel Verleysen, Benoît Frénay

Faculte d'informatique

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférence › Article dans les actes d'une conférence/un colloque

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 originale	Anglais
titre	Artificial Neural Networks and Machine Learning – ICANN 2019
Sous-titre	Deep Learning - 28th International Conference on Artificial Neural Networks, Proceedings
rédacteurs en chef	Igor V. Tetko, Pavel Karpov, Fabian Theis, Vera Kurková
Editeur	Springer Verlag
Pages	59-71
Nombre de pages	13
ISBN (imprimé)	9783030304836
Les DOIs	https://doi.org/10.1007/978-3-030-30484-3_5
Etat de la publication	Publié - 1 janv. 2019
Evénement	28th International Conference on Artificial Neural Networks, ICANN 2019 - Munich, Allemagne Durée: 17 sept. 2019 → 19 sept. 2019

Série de publications

Nom	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11728 LNCS
ISSN (imprimé)	0302-9743
ISSN (Electronique)	1611-3349

Une conférence

Une conférence	28th International Conference on Artificial Neural Networks, ICANN 2019
Pays	Allemagne
La ville	Munich
période	17/09/19 → 19/09/19

Accès au document

10.1007/978-3-030-30484-3_5

Link to publication in Scopus

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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

Degeest, Alexandra ; Verleysen, Michel ; Frénay, Benoît. / Comparison Between Filter Criteria for Feature Selection in Regression. Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning - 28th International Conference on Artificial Neural Networks, Proceedings. Editeur / Igor V. Tetko ; Pavel Karpov ; Fabian Theis ; Vera Kurková. Springer Verlag, 2019. p. 59-71 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Comparison Between Filter Criteria for Feature Selection in Regression",

abstract = "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.",

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Degeest, A, Verleysen, M & Frénay, B 2019, Comparison Between Filter Criteria for Feature Selection in Regression. Dans IV 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. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), VOL. 11728 LNCS, Springer Verlag, p. 59-71, 28th International Conference on Artificial Neural Networks, ICANN 2019, Munich, Allemagne, 17/09/19. https://doi.org/10.1007/978-3-030-30484-3_5

Comparison Between Filter Criteria for Feature Selection in Regression. / Degeest, Alexandra; Verleysen, Michel; Frénay, Benoît.

Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning - 28th International Conference on Artificial Neural Networks, Proceedings. Ed. / Igor V. Tetko; Pavel Karpov; Fabian Theis; Vera Kurková. Springer Verlag, 2019. p. 59-71 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol 11728 LNCS).

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférence › Article dans les actes d'une conférence/un colloque

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T1 - Comparison Between Filter Criteria for Feature Selection in Regression

AU - Degeest, Alexandra

AU - Verleysen, Michel

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PY - 2019/1/1

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N2 - 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.

AB - 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.

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KW - Filter criteria

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BT - Artificial Neural Networks and Machine Learning – ICANN 2019

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