Genetic algorithm-based method for mitigating label noise issue in ECG signal classification

Classification of electrocardiographic (ECG) signals can be deteriorated by the presence in the training set of mislabeled samples. To alleviate this issue we propose a new approach that aims at assisting the human user (cardiologist) in his/her work of labeling by removing in an automatic way the training samples with potential mislabeling problems. The proposed method is based on a genetic optimization process, in which each chromosome represents a candidate solution for validating/invalidating the training samples. Moreover, the optimization process consists of optimizing jointly two different crite-