دورية أكاديمية
ONLINE KERNEL AMGLVQ FOR ARRHYTHMIA HEARBEATS CLASSIFICATION
| العنوان: | ONLINE KERNEL AMGLVQ FOR ARRHYTHMIA HEARBEATS CLASSIFICATION |
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| المؤلفون: | Elly Matul Imah, R. Sulaiman |
| المصدر: | Jurnal Ilmiah Kursor: Menuju Solusi Teknologi Informasi, Vol 8, Iss 4 (2016) |
| بيانات النشر: | Informatics Department, Engineering Faculty, 2016. |
| سنة النشر: | 2016 |
| المجموعة: | LCC:Electronic computers. Computer science |
| مصطلحات موضوعية: | KAMGLVQ, SVM, Backpropagation, arrhythmia, ECG, imbalanced data set, Electronic computers. Computer science, QA75.5-76.95 |
| الوصف: | This study proposes Online Kernel Adaptive Multilayer Generalized Learning Vector Quantization (KAMGLVQ) for handling imbalanced data sets. KAMGLVQ is extended version of AMGLVQ that used kernel function to handling non-linear classification problems. Basically AMGLVQ is vector quantization based learning. The vector quantization based learning is very simple algorithm that can be applied to the multiclass problem and the complexity of LVQ can be controlled during training process. KAMGLVQ works at online kernel learning system that integrating feature extraction and classification. The architecture network of KAMGLVQ consists of three layers, input layer, hidden layer, and an output layer. The hidden layer of KAMGLVQ is adaptive; this algorithm will generate a number of hidden layer nodes. The algorithm implement on real ECG signals from the MIT-BIH arrhythmias database and synthetic data. The experiments showed that KAMGLVQ able improve the accuracy of classification better than SVM or back-propagation NN; also able to reduce the time computational cost. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 0216-0544 2301-6914 |
| Relation: | https://kursorjournal.org/index.php/kursor/article/view/108; https://doaj.org/toc/0216-0544; https://doaj.org/toc/2301-6914 |
| DOI: | 10.28961/kursor.v8i4.108 |
| URL الوصول: | https://doaj.org/article/c639b96e896a484db222329c887c77c7 |
| رقم الأكسشن: | edsdoj.639b96e896a484db222329c887c77c7 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 02160544 23016914 |
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| DOI: | 10.28961/kursor.v8i4.108 |