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

Keywords

  • Feature selection
  • Filter criteria
  • Regression

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  • 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