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

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

Internet

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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