Comparison of ensemble-combination approaches in an automatic sleep staging inter-database generalization task

4 pages•Published: February 16, 2023

Adriana Anido Alonso and Diego Alvarez Estévez

Abstract

Deep learning has demostrated its usefulness in reaching top-level performance. How- ever, inter-database generalization is still a broad of concern due to the aroused differences between local and external datasets’ performances. In this work we explore different deep learning model’s combination strategies applied to a multi-database case of study in the domain of sleep medicine. More specifically, three ensemble combination methods (namely max-voting, output averaging and weighted combination using the Nelder-Mead search) are analyzed in comparison to baseline methods (local models, database assembly approach) in a sleep staging inter-database generalization task.

Keyphrases: cnn, deep learning, domain adaptation, ensemble models, inter database generalization, lstm, sleep medicine

In: Alvaro Leitao and Lucía Ramos (editors). Proceedings of V XoveTIC Conference. XoveTIC 2022, vol 14, pages 170-173.

Links:	https://easychair.org/publications/paper/lFRC
	https://doi.org/10.29007/dwm6

BibTeX entry

@inproceedings{XoveTIC2022:Comparison_ensemble_combination_approaches,
  author    = {Adriana Anido Alonso and Diego Alvarez Estévez},
  title     = {Comparison of ensemble-combination approaches in an automatic sleep staging inter-database generalization task},
  booktitle = {Proceedings of V XoveTIC Conference. XoveTIC 2022},
  editor    = {Alvaro Leitao and Lucía Ramos},
  series    = {Kalpa Publications in Computing},
  volume    = {14},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1762},
  url       = {/publications/paper/lFRC},
  doi       = {10.29007/dwm6},
  pages     = {170-173},
  year      = {2023}}

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