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The classification accuracy of electrocardiogram signal is often affected bydiverse factors in which mislabeled training samples issue is one of the mostinfluential problems. In order to mitigate this negative effect, the method ofcross validation is introduced to identify the mislabeled samples. The methodutilizes the cooperative advantages of different classifiers to act as a filterfor the training samples. The filter removes the mislabeled training samplesand retains the correctly labeled ones with the help of 10-fold crossvalidation. Consequently, a new training set is provided to the finalclassifiers to acquire higher classification accuracies. Finally, wenumerically show the effectiveness of the proposed method with the MIT-BIHarrhythmia database.

Identifying the Mislabeled Training Samples of ECG Signals using Machine Learning