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Combining unsupervised and supervised learning techniques for enhancing the performance of functional data classifiers

Author

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  • Fabrizio Maturo

    (University of Campania Luigi Vanvitelli)

  • Rosanna Verde

    (University of Campania Luigi Vanvitelli)

Abstract

This paper offers a supervised classification strategy that combines functional data analysis with unsupervised and supervised classification methods. Specifically, a two-steps classification technique for high-dimensional time series treated as functional data is suggested. The first stage is based on extracting additional knowledge from the data using unsupervised classification employing suitable metrics. The second phase applies functional supervised classification of the new patterns learned via appropriate basis representations. The experiments on ECG data and comparison with the classical approaches show the effectiveness of the proposed technique and exciting refinement in terms of accuracy. A simulation study with six scenarios is also offered to demonstrate the efficacy of the suggested strategy. The results reveal that this line of investigation is compelling and worthy of further development.

Suggested Citation

  • Fabrizio Maturo & Rosanna Verde, 2024. "Combining unsupervised and supervised learning techniques for enhancing the performance of functional data classifiers," Computational Statistics, Springer, vol. 39(1), pages 239-270, February.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:1:d:10.1007_s00180-022-01259-8
    DOI: 10.1007/s00180-022-01259-8
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    References listed on IDEAS

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    1. Chung Chang & Yakuan Chen & R. Ogden, 2014. "Functional data classification: a wavelet approach," Computational Statistics, Springer, vol. 29(6), pages 1497-1513, December.
    2. Ferraty, F. & Vieu, P., 2003. "Curves discrimination: a nonparametric functional approach," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 161-173, October.
    3. Fabrizio Maturo & Stefania Migliori & Francesco Paolone, 2019. "Measuring and monitoring diversity in organizations through functional instruments with an application to ethnic workforce diversity of the U.S. Federal Agencies," Computational and Mathematical Organization Theory, Springer, vol. 25(4), pages 357-388, December.
    4. Antonio Cuevas & Manuel Febrero & Ricardo Fraiman, 2007. "Robust estimation and classification for functional data via projection-based depth notions," Computational Statistics, Springer, vol. 22(3), pages 481-496, September.
    5. Cristian Preda & Gilbert Saporta & Caroline Lévéder, 2007. "PLS classification of functional data," Computational Statistics, Springer, vol. 22(2), pages 223-235, July.
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