Abstract
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.
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References
Abdelkarim, C., Rejeb, L., Said, L.B., Elarbi, M.: Evidential learning classifier system. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion pp. 123–124 (2017)
Alickovic, E., Subasi, A.: Ensemble SVM method for automatic sleep stage classification. IEEE Trans. Instrument. Measur. 67(6), 1258–1265 (2018)
Amaddeo, A., Sabil, A., Arroyo, J.O., De Sanctis, L., Griffon, L., Baffet, G., Khirani, S., Fauroux, B.: Tracheal sounds for the scoring of sleep respiratory events in children. J. Clin. Sleep Med. 16(3), 361–369 (2020)
Berry, R.B., Brooks, R., Gamaldo, C.E., Harding, S.M., Marcus, C., Vaughn, B.: The aasm manual for the scoring of sleep and associated events. Rules, Terminology and Technical Specifications, Darien, Illinois, American Academy of Sleep Medicine (2012)
Butz, M.V., Wilson, S.W.: An algorithmic description of XCS. In: International Workshop on Learning Classifier Systems, pp. 253–272. Springer (2000)
Del Jesus, M.J., Hoffmann, F., Navascués, L.J., Sánchez, L.: Induction of fuzzy-rule-based classifiers with evolutionary boosting algorithms. IEEE Trans. Fuzzy Syst. 12(3), 296–308 (2004)
Ferjani, R., Rejeb, L., Said, L.B.: Unsupervised sleep stages classification based on physiological signals. In: International Conference on Practical Applications of Agents and Multi-agent Systems pp. 134–145. Springer (2020)
Keller, J.M., Gray, M.R., Givens, J.A.: A fuzzy k-nearest neighbor algorithm. IEEE Trans. Syst. Man Cybern. 4, 580–585 (1985)
Ouanes, A., Rejeb, L.: A hybrid approach for sleep stages classification. In: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, pp. 493–500. ACM (2016)
Pätzel, D., Stein, A., Hähner, J.: A survey of formal theoretical advances regarding XCS. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 1295–1302 (2019)
Phan, H., Andreotti, F., Cooray, N., Chén, O.Y., De Vos, M.: Joint classification and prediction CNN framework for automatic sleep stage classification. IEEE Trans. Biomed. Eng. (2018)
Sánchez, L., Otero, J.: Boosting fuzzy rules in classification problems under single-winner inference. Int. J. Intell. Syst. 22(9), 1021–1034 (2007)
Smets, P.: The transferable belief model for quantified belief representation. In: Quantified Representation of Uncertainty and Imprecision, pp. 267–301. Springer (1998)
Wilson, S.W.: Get real! xcs with continuous-valued inputs. In: International Workshop on Learning Classifier Systems, pp. 209–219. Springer (1999)
Wilson, S.W.: Get real! xcs with continuous-valued inputs. In: Learning Classifier Systems, pp. 209–219. Springer (2000)
Wu, X., Yang, J., Pan, Y., Zhang, X., Luo, Y.: Automatic sleep-stage scoring based on photoplethysmographic signals. Physiol. Measur. (2020)
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Ferjani, R., Rejeb, L., Said, L.B. (2021). Belief eXtended Classifier System: A New Approach for Dealing with Uncertainty in Sleep Stages Classification. In: Abraham, A., Hanne, T., Castillo, O., Gandhi, N., Nogueira Rios, T., Hong, TP. (eds) Hybrid Intelligent Systems. HIS 2020. Advances in Intelligent Systems and Computing, vol 1375. Springer, Cham. https://doi.org/10.1007/978-3-030-73050-5_46
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DOI: https://doi.org/10.1007/978-3-030-73050-5_46
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