Biometrics. 2022 Apr 15. doi: 10.1111/biom.13679. Online ahead of print.
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 two-fold: (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 divergence (KL), Eigen-Distance (ED) and Chernoff divergence (CH). Both simulations for binary and multiclass problems, and an EEG application demonstrate the efficacy of the proposed methods in constructing accurate classifiers with high-dimensional MTS. This article is protected by copyright. All rights reserved.
PMID:35426119 | DOI:10.1111/biom.13679
