Multiclass Support Vector Machines for Classification of ECG Data with Missing Values

The article presents an experimental study on multiclass Support Vector Machine (SVM) methods over a cardiac arrhythmia dataset that has missing attribute values for electrocardiogram (ECG) diagnostic application. The presence of an incomplete dataset and high data dimensionality can affect the perf...

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Veröffentlicht in:	Applied artificial intelligence 2015-08, Vol.29 (7), p.660-674
Hauptverfasser:	Hejazi, Maryamsadat, Al-Haddad, S. A. R., Singh, Yashwant Prasad, Hashim, Shaiful Jahari, Aziz, Ahmad Fazli Abdul
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial intelligence Cardiac arrhythmia Classification Data analysis Data processing Discriminant analysis Electrocardiography Expert systems OAO Performance evaluation Preprocessing Support vector machines
Online-Zugang:	Volltext
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