On Supervised Feature Selection from High Dimensional Feature Spaces

• URL: http://arxiv.org/abs/2203.11924v1
• Date: Tue, 22 Mar 2022 17:52:18 GMT
• Title: On Supervised Feature Selection from High Dimensional Feature Spaces
• Authors: Yijing Yang, Wei Wang, Hongyu Fu and C.-C. Jay Kuo
• Abstract summary: We propose a novel supervised feature selection methodology for machine learning decisions. Tests are called the discriminant feature test (DFT) and the relevant feature test (RFT) for the classification and regression problems. It is shown that DFT and RFT can select a lower dimensional feature subspace distinctly and robustly while maintaining high decision performance.
• Score: 33.22006437399753
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. 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.

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