Label Noise-Tolerant Hidden Markov Models for Segmentation: Application to ECGs

Benoît Frénay, Gaël de Lannoy, Michel Verleysen

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Résumé

The performance of traditional classification models can adversely be impacted by the presence of label noise in training observations. The pioneer work of Lawrence and Schölkopf tackled this issue in datasets with independent observations by incorporating a statistical noise model within the inference algorithm. In this paper, the specific case of label noise in non-independent observations is rather considered. For this purpose, a label noise-tolerant expectation-maximisation algorithm is proposed in the frame of hidden Markov models. Experiments are carried on both healthy and pathological electrocardiogram signals with distinct types of additional artificial label noise. Results show that the proposed label noise-tolerant inference algorithm can improve the segmentation performances in the presence of label noise. © 2011 Springer-Verlag.

langue originaleAnglais
titreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages455-470
Nombre de pages16
Volume6911 LNAI
EditionPART 1
Les DOIs
Etat de la publicationPublié - 2011
Modification externeOui
EvénementEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2011 - Athens, Grèce
Durée: 5 sept. 20119 sept. 2011

Série de publications

NomLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
nombrePART 1
Volume6911 LNAI
ISSN (imprimé)03029743
ISSN (Electronique)16113349

Une conférence

Une conférenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2011
PaysGrèce
La villeAthens
période5/09/119/09/11

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  • Contient cette citation

    Frénay, B., de Lannoy, G., & Verleysen, M. (2011). Label Noise-Tolerant Hidden Markov Models for Segmentation: Application to ECGs. Dans Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (PART 1 Ed., Vol 6911 LNAI, p. 455-470). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol 6911 LNAI, Numéro PART 1). https://doi.org/10.1007/978-3-642-23780-5_39