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A Noise-Aware Multiple Imputation Algorithm for Missing Data

Author

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  • Fangfang Li

    (School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China)

  • Hui Sun

    (School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China)

  • Yu Gu

    (School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China)

  • Ge Yu

    (School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China)

Abstract

Missing data is a common and inevitable phenomenon. In practical applications, the datasets usually contain noises for various reasons. Most of the existing missing data imputing algorithms are affected by noises which reduce the accuracy of the imputation. This paper proposes a noise-aware missing data multiple imputation algorithm NPMI in static data. Different multiple imputation models are proposed according to the missing mechanism of data. Secondly, the method to determine the imputation order of multivariablesmissing is given. A random sampling consistency algorithm is proposed to estimate the initial values of the parameters of the multiple imputation model to reduce the influence of noise data and improve the algorithm’s robustness. Experiments on two real datasets and two synthetic datasets verify the accuracy and efficiency of the proposed NPMI algorithm, and the results are analyzed.

Suggested Citation

  • Fangfang Li & Hui Sun & Yu Gu & Ge Yu, 2022. "A Noise-Aware Multiple Imputation Algorithm for Missing Data," Mathematics, MDPI, vol. 11(1), pages 1-16, December.
    • Handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:73-:d:1014308
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    References listed on IDEAS

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    2. Sascha Lindig & Atse Louwen & David Moser & Marko Topic, 2020. "Outdoor PV System Monitoring—Input Data Quality, Data Imputation and Filtering Approaches," Energies, MDPI, vol. 13(19), pages 1-18, September.
    3. Zhiwei Fu & Bruce L. Golden & Shreevardhan Lele & S. Raghavan & Edward A. Wasil, 2003. "A Genetic Algorithm-Based Approach for Building Accurate Decision Trees," INFORMS Journal on Computing, INFORMS, vol. 15(1), pages 3-22, February.
    4. Lai, Peng & Wang, Qihua, 2014. "Semiparametric efficient estimation for partially linear single-index models with responses missing at random," Journal of Multivariate Analysis, Elsevier, vol. 128(C), pages 33-50.
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