Multiclass support vector machines for classification of ECG data with missing values

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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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Bibliographic Details
Main Authors:	Hejazi, Maryamsadat, Syed Mohamed, Syed Abdul Rahman Al-Haddad, Singh, Yashwant Prasad, Hashim, Shaiful Jahari, Abdul Aziz, Ahmad Fazli
Format:	Article
Language:	English
Published:	Taylor & Francis 2015
Online Access:	http://psasir.upm.edu.my/id/eprint/52401/ http://psasir.upm.edu.my/id/eprint/52401/1/Multiclass%20support%20vector%20machines%20for%20classification%20of%20ECG%20data%20with%20missing%20values.pdf

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