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A kernel PLS based classification method with missing data handling

Author

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  • Thuy Tuong Nguyen

    (University of California)

  • Yury Tsoy

    (Institut Pasteur Korea)

Abstract

We provide a data classification mechanism with missing data handling based on kernel partial least squares (kernel PLS) and discriminant analysis (kernel PLSDA). The novelty of the method is that class variables are used for validation of the missing values imputation. Likewise, this paper is first in utilizing the kernel PLS in handling and classifying missing data. By experimentally comparing the results of different classification methods including missing data handling on three opened biomedical datasets (Arrhythmia, Mammographic Mass, and Pima Indians Diabetes at UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/datasets.html ), we found that the proposed kernel PLS plus kernel PLSDA yielded better accuracies than the existing methods.

Suggested Citation

  • Thuy Tuong Nguyen & Yury Tsoy, 2017. "A kernel PLS based classification method with missing data handling," Statistical Papers, Springer, vol. 58(1), pages 211-225, March.
    • Handle: RePEc:spr:stpapr:v:58:y:2017:i:1:d:10.1007_s00362-015-0694-y
    DOI: 10.1007/s00362-015-0694-y
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    Cited by:

    1. Jeongsub Choi & Youngdoo Son & Myong K. Jeong, 2024. "Gaussian kernel with correlated variables for incomplete data," Annals of Operations Research, Springer, vol. 341(1), pages 223-244, October.
    2. Yu-Ye Zou & Han-Ying Liang, 2020. "CLT for integrated square error of density estimators with censoring indicators missing at random," Statistical Papers, Springer, vol. 61(6), pages 2685-2714, December.

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