首页|Imbalanced ECG data classification using a novel model based on active training subset selection and modified broad learning system
Imbalanced ECG data classification using a novel model based on active training subset selection and modified broad learning system
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NSTL
Elsevier
? 2022 Elsevier LtdThis paper classifies non-ectopic (N), supraventricular ectopic (S), ventricular ectopic (V), and fusion (F) beats in the MIT-BIH arrhythmia database. The classification encounters serious class imbalance since the number of beats in N (majority class) with sample number above the average per class is heavily outnumbered than that in S, V, and F (minority classes) with sample number below the average per class. To address the class imbalance, a novel model based on active training subset selection and modified broad learning system (MBLS) is proposed. In each iteration, the MBLS trained with the current training subset is used to predict the class label of the test sample and actively select a new training subset for the next iteration. Finally, the class of the test sample is determined by voting on the predictions of all iterations. The experimental results show that our method has excellent performance and outperforms the existing methods.
Active training subset selectionClass imbalanceECG arrhythmia classificationModified broad learning systemVoting methods
Fan W.、Si Y.、Yang W.、Sun M.
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College of Communication Engineering Jilin University
NSTL
Elsevier
