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Quantum-enhanced feature selection with forward selection and backward elimination

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Abstract

Feature selection is a well-known preprocessing technique in machine learning, which can remove irrelevant features to improve the generalization capability of a classifier and reduce training and inference time. However, feature selection is time-consuming, particularly for the applications those have thousands of features, such as image retrieval, text mining and microarray data analysis. It is crucial to accelerate the feature selection process. We propose a quantum version of wrapper-based feature selection, which converts a classical feature selection to its quantum counterpart. It is valuable for machine learning on quantum computer. In this paper, we focus on two popular kinds of feature selection methods, i.e., wrapper-based forward selection and backward elimination. The proposed feature selection algorithm can quadratically accelerate the classical one.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant Nos. 61472452, 61772565, 61602116, 61502179), the Natural Science Foundation of Guangdong Province of China (Grant No. 2017A030313378), the Science and Technology Program of Guangzhou City of China (No. 201707010194), the Fundamental Research Funds for the Central Universities (No. 17lgzd29) and the Research Foundation for Talented Scholars of Foshan University (No. gg040996).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, Foshan University, Foshan, 528000, China

    Zhimin He

  2. School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, 510006, China

    Lvzhou Li

  3. School of Economics and Management, Wuyi University, Jiangmen, 529020, China

    Zhiming Huang

  4. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642, China

    Haozhen Situ

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  1. Zhimin He
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  2. Lvzhou Li
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Correspondence to Haozhen Situ.

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He, Z., Li, L., Huang, Z. et al. Quantum-enhanced feature selection with forward selection and backward elimination. Quantum Inf Process 17, 154 (2018). https://doi.org/10.1007/s11128-018-1924-8

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