https://scholars.lib.ntu.edu.tw/handle/123456789/611201
標題: | A scalable extreme learning machine (S-ELM) for class-incremental ECG-based user identification | 作者: | AN-YEU(ANDY) WU | 關鍵字: | Class-incremental learning; ECG identification; Extreme learning machine | 公開日期: | 2021 | 卷: | 2021-May | 來源出版物: | Proceedings - IEEE International Symposium on Circuits and Systems | 摘要: | User identification using electrocardiogram (ECG) is emerging due to the uniqueness and convenience of ECG signals. In addition, in real world applications, new subjects may enter the existed identification system and be authorized to access the private data. Therefore, we propose a scalable extreme learning machine (S-ELM) to meet the demand for class-incremental ECG-based user identification. We first prove that the output weight of the S-ELM learnt in a class-incremental manner is as same as that of a regular ELM which prior has the information of the number of total class. Therefore, in our experiment, S-ELM immunes from the catastrophic forgetting phenomenon, which is a common problem in class-incremental scenarios. Comparing to another class-incremental extreme learning machine such as progressive ELM, S-ELM outperforms progressive ELM by 7% accuracy in online dataset. Comparing to another commonly applied classifier, support vector machine (SVM) with linear and radial basis function (RBF) kernels, S-ELM shows its efficiency by 13.35% and 10.54% higher accuracy but only spends 5.09% and 3.48% of the inference time. Therefore, the proposed S-ELM is promising for the class-incremental ECG-based user identification. © 2021 IEEE |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85109032501&doi=10.1109%2fISCAS51556.2021.09401716&partnerID=40&md5=97f634cf0967cee5cd1e59d717fe414e https://scholars.lib.ntu.edu.tw/handle/123456789/611201 |
ISBN: | 9.78173E+12 | ISSN: | 02714310 | 其他識別: | PICSD | DOI: | 10.1109/ISCAS51556.2021.09401716 | SDG/關鍵字: | Electrocardiography; Knowledge acquisition; Support vector machines; Catastrophic forgetting; ECG signals; Extreme learning machine; Incremental extreme learning machine; Its efficiencies; Private data; Radial Basis Function(RBF); User identification; Learning systems |
顯示於: | 電機工程學系 |
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