ABSTRACT
Sparse functional data are commonly observed in real-data analyzes. For such data, we propose a new classification method based on functional principal component analysis (FPCA) and bootstrap aggregating. Bootstrap aggregating is believed to improve the single classifier. In this paper, we apply this belief to an FPCA based classification, and compare the classification performance with that of the single classifiers. The simulation results show that the proposed method performs better than the conventional single classifiers. We then conduct two real-data analyzes.
2010 Mathematics Subject Classification:
Acknowledgments
This research is supported by the National Research Foundation of Korea (NRF) funded by the Korea government (2019R1A2C4069453).
Disclosure statement
No potential conflict of interest was reported by the author(s).