  The classification accuracy of electrocardiogram signal is often affected by
diverse factors in which mislabeled training samples issue is one of the most
influential problems. In order to mitigate this negative effect, the method of
cross validation is introduced to identify the mislabeled samples. The method
utilizes the cooperative advantages of different classifiers to act as a filter
for the training samples. The filter removes the mislabeled training samples
and retains the correctly labeled ones with the help of 10-fold cross
validation. Consequently, a new training set is provided to the final
classifiers to acquire higher classification accuracies. Finally, we
numerically show the effectiveness of the proposed method with the MIT-BIH
arrhythmia database.
