Genetic algorithm-based method for mitigating label noise issue in ECG signal classification
We propose a method for mitigating label noise issue in ECG signal classification. It is based on a completely automatic genetic optimization process. Statistical separability and number of invalidated samples are considered. Experiments are conducted on real signals from the MIT-BIH arrhythmia database. Improvements in terms of classification accuracy are demonstrated.
ecg signal classification genetic algorithms mislabeling issue multiobjective optimization training sample validation