Impact of Label Noise on the Learning Based Models for a Binary Classification of Physiological Signal.

ABSTRACT: Label noise is omnipresent in the annotations process and has an impact on supervised learning algorithms. This work focuses on the impact of label noise on the performance of learning models by examining the effect of random and class-dependent label noise on a binary classification task: quality assessment for photoplethysmography (PPG). PPG signal is used to detect physiological changes and its quality can have a significant impact on the subsequent tasks, which makes PPG quality assessment a particularly good target for examining the impact of label noise in the field of biomedicine. Random and class-dependent label noise was introduced separately into the training set to emulate the errors associated with fatigue and bias in labeling data samples. We also tested different representati

SUBMITTER: Ding C 

PROVIDER: S-EPMC9572105 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature