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- W2912253350 abstract "ECG-based heartbeat classification is often accompanied with difficult feature extraction and imbalanced sampling data. In order to alleviate the bias in performance caused by imbalanced data, a Selective Ensemble Learning Framework based on sample Distribution and classifier Diversity (SELFrame-DD) is proposed for ECG-based heartbeat classification. In SELFrame-DD, an improved SMOTE algorithm is proposed to generate training sets by using a sample-distribution based resampling strategy, and the selective ensemble depends on the diversity of classifiers and the prediction accuracy of classifiers for minority classes. Besides, a multimodal ECG feature extraction is employed based on wavelet packet decomposition and 1-D convolutional neural network. Experimental studies on MIT-BIH arrhythmia database show that the proposed algorithm can achieve a high classification accuracy for imbalanced multi-category classification." @default.
- W2912253350 created "2019-02-21" @default.
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- W2912253350 date "2018-12-01" @default.
- W2912253350 modified "2023-09-25" @default.
- W2912253350 title "A Selective Ensemble Learning Framework for ECG-Based Heartbeat Classification with Imbalanced Data" @default.
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- W2912253350 doi "https://doi.org/10.1109/bibm.2018.8621523" @default.
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