Gradient based efficient feature selection

    Research output: Chapter in Book/Conference paperConference paper

    3 Citations (Scopus)

    Abstract

    Selecting a reduced set of relevant and non-redundant features for supervised classification problems is a challenging task. We propose a gradient based feature selection method which can search the feature space efficiently and select a reduced set of representative features. We test our proposed algorithm on five small and medium sized pattern classification datasets as well as two large 3D face datasets for computer vision applications. Comparison with the state of the art wrapper and filter methods shows that our proposed technique yields better classification results in lesser number of evaluations of the target classifier. The feature subset selected by our algorithm is representative of the classes in the data and has the least variation in classification accuracy. © 2014 IEEE.
    Original languageEnglish
    Title of host publication2014 IEEE Winter Conference on Applications of Computer Vision (WACV)
    Place of PublicationUnited States
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages191-197
    ISBN (Print)9781479949854
    DOIs
    Publication statusPublished - 2014
    Event2014 IEEE Winter Conference on Applications of Computer Vision - Steamboat Springs, United States
    Duration: 24 Mar 201426 Mar 2014

    Conference

    Conference2014 IEEE Winter Conference on Applications of Computer Vision
    CountryUnited States
    CitySteamboat Springs
    Period24/03/1426/03/14

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  • Cite this

    Gilani, S. Z. A., Shafait, F., & Mian, A. (2014). Gradient based efficient feature selection. In 2014 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 191-197). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/WACV.2014.6836102