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

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...

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Bibliographic Details
Published in:Computational statistics 2024-02, Vol.39 (1), p.239-270
Main Authors: Maturo, Fabrizio, Verde, Rosanna
Format: Article
Language:English
Subjects:
Classification
Clustering
Data analysis
Economic Theory/Quantitative Economics/Mathematical Methods
Electrocardiography
Machine learning
Mathematical functions
Mathematics and Statistics
Original Paper
Performance enhancement
Probability and Statistics in Computer Science
Probability Theory and Stochastic Processes
Statistics
Supervised learning
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Summary: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.
ISSN:0943-4062
1613-9658
DOI:10.1007/s00180-022-01259-8