APSIPA Transactions on Signal and Information Processing > Vol 11 > Issue 1

On Supervised Feature Selection from High Dimensional Feature Spaces

Yijing Yang, University of Southern California, USA, yijingya@usc.edu , Wei Wang, University of Southern California, USA, Hongyu Fu, University of Southern California, USA, C.-C. Jay Kuo, University of Southern California, USA
 
Suggested Citation
Yijing Yang, Wei Wang, Hongyu Fu and C.-C. Jay Kuo (2022), "On Supervised Feature Selection from High Dimensional Feature Spaces", APSIPA Transactions on Signal and Information Processing: Vol. 11: No. 1, e31. http://dx.doi.org/10.1561/116.00000016

Publication Date: 19 Oct 2022
© 2022 Y. Yang, W. Wang, H. Fu and C.-C. J. Kuo
 
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Keywords
Machine learningclassificationregressionsupervised feature selection
 

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This is published under the terms of CC BY-NC.

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In this article:
Introduction 
Review of Previous Work 
Proposed Feature Selection Methods 
Experimental Results 
Conclusion and Future Work 
References 

Abstract

The application of machine learning to image and video data often yields a high dimensional feature space. Effective feature selection techniques identify a discriminant feature subspace that lowers computational and modeling costs with little performance degradation. A novel supervised feature selection methodology is proposed for machine learning decisions in this work. The resulting tests are called the discriminant feature test (DFT) and the relevant feature test (RFT) for the classification and regression problems, respectively. The DFT and RFT procedures are described in detail. Furthermore, we compare the effectiveness of DFT and RFT with several classic feature selection methods. To this end, we use deep features obtained by LeNet-5 for MNIST and Fashion-MNIST datasets as illustrative examples. Other datasets with handcrafted and gene expressions features are also included for performance evaluation. It is shown by experimental results that DFT and RFT can select a lower dimensional feature subspace distinctly and robustly while maintaining high decision performance.

DOI:10.1561/116.00000016