Gradient based efficient feature selection

S.Z.A. Gilani; Faisal Shafait; Ajmal Mian

doi:10.1109/WACV.2014.6836102

Gradient based efficient feature selection

S.Z.A. Gilani, Faisal Shafait, Ajmal Mian

Research output: Chapter in Book/Conference paper › Conference 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 language	English
Title of host publication	2014 IEEE Winter Conference on Applications of Computer Vision (WACV)
Place of Publication	United States
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	191-197
ISBN (Print)	9781479949854
DOIs	https://doi.org/10.1109/WACV.2014.6836102
Publication status	Published - 2014
Event	2014 IEEE Winter Conference on Applications of Computer Vision - Steamboat Springs, United States Duration: 24 Mar 2014 → 26 Mar 2014

Conference

Conference	2014 IEEE Winter Conference on Applications of Computer Vision
Country	United States
City	Steamboat Springs
Period	24/03/14 → 26/03/14

Access to Document

10.1109/WACV.2014.6836102

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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

@inproceedings{123c491dd14c43a49d0b952b389be222,

title = "Gradient based efficient feature selection",

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. {\textcopyright} 2014 IEEE.",

author = "S.Z.A. Gilani and Faisal Shafait and Ajmal Mian",

year = "2014",

doi = "10.1109/WACV.2014.6836102",

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pages = "191--197",

booktitle = "2014 IEEE Winter Conference on Applications of Computer Vision (WACV)",

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note = "2014 IEEE Winter Conference on Applications of Computer Vision ; Conference date: 24-03-2014 Through 26-03-2014",

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Gilani, SZA , Shafait, F & Mian, A 2014, Gradient based efficient feature selection. in 2014 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 191-197, 2014 IEEE Winter Conference on Applications of Computer Vision, Steamboat Springs, United States, 24/03/14. https://doi.org/10.1109/WACV.2014.6836102

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