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...

Full description

Saved in:
Bibliographic Details
Published in:Applied artificial intelligence 2015-08, Vol.29 (7), p.660-674
Main Authors: Hejazi, Maryamsadat, Al-Haddad, S. A. R., Singh, Yashwant Prasad, Hashim, Shaiful Jahari, Aziz, Ahmad Fazli Abdul
Format: Article
Language:English
Subjects:
Artificial intelligence
Cardiac arrhythmia
Classification
Data analysis
Data processing
Discriminant analysis
Electrocardiography
Expert systems
OAO
Performance evaluation
Preprocessing
Support vector machines
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0883-9514
1087-6545
DOI:10.1080/08839514.2015.1051887