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

• 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
• ISSN: 0943-4062; 1613-9658
• DOI: 10.1007/s00180-022-01259-8

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.