Automatic diagnosis of multiple cardiac abnormalities from reduced-lead electrocardiogram (ECG) data is challenging. One of the reasons for this is the difficulty of defining labels from standard 12-lead data. Reduced-lead ECG data usually do not have identical characteristics of cardiac abnormalities because of the noisy label problem. Thus, there is an inconsistency in the annotated labels between the reduced-lead and 12-lead ECG data. To solve this, we propose deep neural network (DNN)-based ECG classifier models that incorporate DivideMix and stochastic weight averaging (SWA). DivideMix was used to refine the noisy label by using two separate models. Besides DivideMix, we used a model ensemble technique, SWA, which also focuses on the noisy label problem, to enhance the effect of the models generated by DivideMix. Our classifiers received scores of 0.623, 0.593, 0.606, 0.612, and 0.601 (ranked 99th, 99th, 99th, 99th, and 99th, respectively, out of 100 teams) for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead versions, respectively, of the hidden validation set with the challenge evaluation metric.