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Random projection ensemble classification with high‐dimensional time series

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  • Fuli Zhang
  • Kung‐Sik Chan

Abstract

Multivariate time‐series (MTS) data are prevalent in diverse domains and often high dimensional. We propose new random projection ensemble classifiers with high‐dimensional MTS. The method first applies dimension reduction in the time domain via randomly projecting the time‐series variables into some low‐dimensional space, followed by measuring the disparity via some novel base classifier between the data and the candidate generating processes in the projected space. Our contributions are twofold: (i) We derive optimal weighted majority voting schemes for pooling information from the base classifiers for multiclass classification and (ii) we introduce new base frequency‐domain classifiers based on Whittle likelihood (WL), Kullback‐Leibler (KL) divergence, eigen‐distance (ED), and Chernoff (CH) divergence. Both simulations for binary and multiclass problems, and an Electroencephalogram (EEG) application demonstrate the efficacy of the proposed methods in constructing accurate classifiers with high‐dimensional MTS.

Suggested Citation

  • Fuli Zhang & Kung‐Sik Chan, 2023. "Random projection ensemble classification with high‐dimensional time series," Biometrics, The International Biometric Society, vol. 79(2), pages 964-974, June.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:2:p:964-974
    DOI: 10.1111/biom.13679
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    References listed on IDEAS

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    1. Timothy I. Cannings & Richard J. Samworth, 2017. "Random-projection ensemble classification," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 959-1035, September.
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