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. © 2014 IEEE.
Cite
Text
Gilani et al. "Gradient Based Efficient Feature Selection." IEEE/CVF Winter Conference on Applications of Computer Vision, 2014. doi:10.1109/WACV.2014.6836102Markdown
[Gilani et al. "Gradient Based Efficient Feature Selection." IEEE/CVF Winter Conference on Applications of Computer Vision, 2014.](https://mlanthology.org/wacv/2014/gilani2014wacv-gradient/) doi:10.1109/WACV.2014.6836102BibTeX
@inproceedings{gilani2014wacv-gradient,
title = {{Gradient Based Efficient Feature Selection}},
author = {Gilani, Syed Zulqarnain and Shafait, Faisal and Mian, Ajmal S.},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2014},
pages = {191-197},
doi = {10.1109/WACV.2014.6836102},
url = {https://mlanthology.org/wacv/2014/gilani2014wacv-gradient/}
}