2021

Conférence

In International Conference on Hybrid Intelligent Systems (pp. 454-463). Cham: Springer International Publishing.

Sleep is an essential element that affects directly our daily life thus sleep analysis is a very interesting field. Sleep stages classification represents the base of all sleep analysis activities. However, the classification of sleep stages suffers from high uncertainty between its stages which could lead to degrade the performance of classification methods. To cope partially with this issue, we propose a new approach that deals with uncertainty especially with imprecision. Our method integrates the belief function theory in eXtended Classifier System (XCS). The proposed approach shows a good performance ability comparing to classical methods.

@incollection{Ferjani2021Belief,
author = {Rahma Ferjani and Lilia Rejeb and Lamjed Ben Said},
title = {Belief eXtended Classifier System: A New Approach for Dealing with Uncertainty in Sleep Stages Classification},
booktitle = {Hybrid Intelligent Systems, HIS 2020. Advances in Intelligent Systems and Computing},
volume = {1375},
pages = {454--463},
year = {2021},
editor = {Ajith Abraham and Thomas Hanne and Oscar Castillo and Niketa Gandhi and Tatiane Nogueira Rios and Tzung-Pei Hong},
publisher = {Springer, Cham},
doi = {10.1007/978-3-030-73050-5_46},
}