Feature ranking in changing environments where new features are introduced

Alexandra Degeest, Michel Verleysen, Benoît Frénay

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

Abstract

Feature selection and taking into account dynamic environments are two important aspects of modern data analysis and machine learning. In particular, performing feature selection on datasets where the latest instances contain more features than the initial ones is a problem that may be encountered in many application areas where new sensors are acquired. This paper proposes a method for incremental feature selection with rankings combining the information extracted before and after the introduction of new features, even when the number of instances that include these new features is small. Results on three real-world datasets show that using the ranking of features on the original, smaller-dimensional dataset improves the feature selection results performed on the new, larger-dimensional dataset.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-8
Number of pages8
Volume2015-September
ISBN (Print)9781479919604
DOIs
Publication statusPublished - 28 Sep 2015
EventInternational Joint Conference on Neural Networks, IJCNN 2015 - Killarney, Ireland
Duration: 12 Jul 201517 Jul 2015

Conference

ConferenceInternational Joint Conference on Neural Networks, IJCNN 2015
CountryIreland
CityKillarney
Period12/07/1517/07/15

Keywords

  • Linear matrix inequalities
  • Reliability
  • Silicon

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  • Cite this

    Degeest, A., Verleysen, M., & Frénay, B. (2015). Feature ranking in changing environments where new features are introduced. In Proceedings of the International Joint Conference on Neural Networks (Vol. 2015-September, pp. 1-8). [7280533] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2015.7280533