Comparison Between Filter Criteria for Feature Selection in Regression

Alexandra Degeest, Michel Verleysen, Benoît Frénay

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2019
Subtitle of host publicationDeep Learning - 28th International Conference on Artificial Neural Networks, Proceedings
EditorsIgor V. Tetko, Pavel Karpov, Fabian Theis, Vera Kurková
PublisherSpringer Verlag
Pages59-71
Number of pages13
ISBN (Print)9783030304836
DOIs
Publication statusPublished - 1 Jan 2019
Event28th International Conference on Artificial Neural Networks, ICANN 2019 - Munich, Germany
Duration: 17 Sep 201919 Sep 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11728 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference28th International Conference on Artificial Neural Networks, ICANN 2019
CountryGermany
CityMunich
Period17/09/1919/09/19

Fingerprint

Feature Selection
Feature extraction
Filter Method
Regression
Filter
High-dimensional Data
Learning Process
Subset
Model

Keywords

  • Feature selection
  • Filter criteria
  • Regression

Cite this

Degeest, A., Verleysen, M., & Frénay, B. (2019). Comparison Between Filter Criteria for Feature Selection in Regression. In 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 (pp. 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. editor / Igor V. Tetko ; Pavel Karpov ; Fabian Theis ; Vera Kurková. Springer Verlag, 2019. pp. 59-71 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{5fb12a261f12439e8291f2d0ecf64e9b,
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.",
keywords = "Feature selection, Filter criteria, Regression",
author = "Alexandra Degeest and Michel Verleysen and Beno{\^i}t Fr{\'e}nay",
year = "2019",
month = "1",
day = "1",
doi = "10.1007/978-3-030-30484-3_5",
language = "English",
isbn = "9783030304836",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "59--71",
editor = "Tetko, {Igor V.} and Pavel Karpov and Fabian Theis and Vera Kurkov{\'a}",
booktitle = "Artificial Neural Networks and Machine Learning – ICANN 2019",
address = "Germany",

}

Degeest, A, Verleysen, M & Frénay, B 2019, Comparison Between Filter Criteria for Feature Selection in Regression. in 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, pp. 59-71, 28th International Conference on Artificial Neural Networks, ICANN 2019, Munich, Germany, 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).

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

TY - GEN

T1 - Comparison Between Filter Criteria for Feature Selection in Regression

AU - Degeest, Alexandra

AU - Verleysen, Michel

AU - Frénay, Benoît

PY - 2019/1/1

Y1 - 2019/1/1

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.

KW - Feature selection

KW - Filter criteria

KW - Regression

UR - http://www.scopus.com/inward/record.url?scp=85072873687&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-30484-3_5

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

M3 - Conference contribution

AN - SCOPUS:85072873687

SN - 9783030304836

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 59

EP - 71

BT - Artificial Neural Networks and Machine Learning – ICANN 2019

A2 - Tetko, Igor V.

A2 - Karpov, Pavel

A2 - Theis, Fabian

A2 - Kurková, Vera

PB - Springer Verlag

ER -

Degeest A, Verleysen M, Frénay B. Comparison Between Filter Criteria for Feature Selection in Regression. In Tetko IV, Karpov P, Theis F, Kurková V, editors, 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