Label noise-tolerant hidden Markov models for segmentation: application to ECGs

Authors:
Benoît Frénay;Gaël de Lannoy;Michel Verleysen
Affiliations:
ICTEAM Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium;ICTEAM Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium;ICTEAM Institute, Université catholique de Louvain, Louvain-la-Neuve, Belgium
Venue:
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Year:
2011

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Abstract

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.