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- W2293623521 abstract "The support vector machines (SVM) are a powerful approach which has been applied in many difficult applications. However, they are principally designed for binary classification problems, and usually their extension for multiclass problems involves only bi-class SVM. In this study, a recently developed machine named the quadratic loss multi-class SVM (M-SVM2), which considers all classes simultaneously, is proposed to classify five different arrhythmia, in addition to normal beat. The M-SVM2 is compared with both decomposition methods involving 2-norm binary SVM (l2-SVM) based on 1-against-1, and 1-against-all approach. The proposed model achieved an average accuracy of (99.73%), which was better compared to the other implemented classifiers. On the other hand, the study showed that post-processing the outputs of the M-SVM2 in terms of probability can significantly improve the classification decision. This results have shown the effectiveness of the proposed approach to enhance the performance of Electrocardiogram (EEG) classification methods." @default.
- W2293623521 created "2016-06-24" @default.
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- W2293623521 date "2016-01-31" @default.
- W2293623521 modified "2023-09-27" @default.
- W2293623521 title "Heartbeats Arrhythmia Classification Using Quadratic Loss Multi-Class Support Vector Machines" @default.
- W2293623521 doi "https://doi.org/10.15866/irecos.v11i1.8280" @default.
- W2293623521 hasPublicationYear "2016" @default.
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