Bootstrap aggregated classification for sparse functional data

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.

Keywords:

• Functional data
• functional principal component analysis
• bootstrap aggregating
• classification
• sparse data

2010 Mathematics Subject Classification:

• 35Q62

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).

Additional information

Funding

This research is supported by the National Research Foundation of Korea (NRF) funded by the Korea government (2019R1A2C4069453) and Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20199710100060).