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Annals of Data Science

Supervised Feature Selection via Quadratic Surface Regression with -Norm Regularization

  • 15-02-2024
Published in:
Authors
Changlin Wang
Zhixia Yang
Junyou Ye
Xue Yang
Manchen Ding
Published in
Annals of Data Science | Issue 2/2024

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Abstract

The article introduces a novel feature selection method called Quadratic Surface Regression for Feature Selection (QSR-FS), which integrates quadratic functions into the least squares loss function and incorporates -norm regularization to obtain sparse solutions. The method is designed to identify the most discriminative features, thereby improving classification accuracy and computational efficiency. The authors propose an alternating iteration algorithm to solve the optimization problem and demonstrate the effectiveness of QSR-FS through extensive experiments on various datasets. The results show that QSR-FS outperforms several classical feature selection methods in terms of classification accuracy and training time, making it a promising approach for high-dimensional data analysis.
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previous article Impact of COVID-19 on Stock Indices Volatility: Long-Memory Persistence, Structural Breaks, or Both?
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Title
Supervised Feature Selection via Quadratic Surface Regression with -Norm Regularization
Authors
Changlin Wang
Zhixia Yang
Junyou Ye
Xue Yang
Manchen Ding
Publication date
15-02-2024
Publisher
Springer Berlin Heidelberg
Published in
Annals of Data Science / Issue 2/2024
Print ISSN: 2198-5804
Electronic ISSN: 2198-5812
DOI
https://doi.org/10.1007/s40745-024-00518-3
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