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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis
2015
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| Online Access: | 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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| author | Hejazi, Maryamsadat Syed Mohamed, Syed Abdul Rahman Al-Haddad Singh, Yashwant Prasad Hashim, Shaiful Jahari Abdul Aziz, Ahmad Fazli |
| author_facet | Hejazi, Maryamsadat Syed Mohamed, Syed Abdul Rahman Al-Haddad Singh, Yashwant Prasad Hashim, Shaiful Jahari Abdul Aziz, Ahmad Fazli |
| author_sort | Hejazi, Maryamsadat |
| collection | UPM |
| description | 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 performance of classifiers. Imputation of missing data and discriminant analysis are commonly used as preprocessing techniques in such large datasets. The article proposes experiments to evaluate performance of One-Against-All (OAA) and One-Against-One (OAO) approaches in kernel multiclass SVM for a heartbeat classification problem with imputation and dimension reduction techniques. The results indicate that the OAA approach has superiority over OAO in multiclass SVM for ECG data analysis with missing values. |
| first_indexed | 2024-03-06T09:15:48Z |
| format | Article |
| id | upm.eprints-52401 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T09:15:48Z |
| publishDate | 2015 |
| publisher | Taylor & Francis |
| record_format | dspace |
| spelling | upm.eprints-524012017-06-06T08:28:09Z http://psasir.upm.edu.my/id/eprint/52401/ Multiclass support vector machines for classification of ECG data with missing values Hejazi, Maryamsadat Syed Mohamed, Syed Abdul Rahman Al-Haddad Singh, Yashwant Prasad Hashim, Shaiful Jahari Abdul Aziz, Ahmad Fazli 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 performance of classifiers. Imputation of missing data and discriminant analysis are commonly used as preprocessing techniques in such large datasets. The article proposes experiments to evaluate performance of One-Against-All (OAA) and One-Against-One (OAO) approaches in kernel multiclass SVM for a heartbeat classification problem with imputation and dimension reduction techniques. The results indicate that the OAA approach has superiority over OAO in multiclass SVM for ECG data analysis with missing values. Taylor & Francis 2015 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/52401/1/Multiclass%20support%20vector%20machines%20for%20classification%20of%20ECG%20data%20with%20missing%20values.pdf Hejazi, Maryamsadat and Syed Mohamed, Syed Abdul Rahman Al-Haddad and Singh, Yashwant Prasad and Hashim, Shaiful Jahari and Abdul Aziz, Ahmad Fazli (2015) Multiclass support vector machines for classification of ECG data with missing values. Applied Artificial Intelligence, 29 (7). pp. 660-674. ISSN 0883-9514; ESSN: 1087-6545 http://www.tandfonline.com/doi/abs/10.1080/08839514.2015.1051887?journalCode=uaai20 10.1080/08839514.2015.1051887 |
| spellingShingle | Hejazi, Maryamsadat Syed Mohamed, Syed Abdul Rahman Al-Haddad Singh, Yashwant Prasad Hashim, Shaiful Jahari Abdul Aziz, Ahmad Fazli Multiclass support vector machines for classification of ECG data with missing values |
| title | Multiclass support vector machines for classification of ECG data with missing values |
| title_full | Multiclass support vector machines for classification of ECG data with missing values |
| title_fullStr | Multiclass support vector machines for classification of ECG data with missing values |
| title_full_unstemmed | Multiclass support vector machines for classification of ECG data with missing values |
| title_short | Multiclass support vector machines for classification of ECG data with missing values |
| title_sort | multiclass support vector machines for classification of ecg data with missing values |
| url | 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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