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Classifying Mass Spectral Data Using SVM and Wavelet-Based Feature Extraction

Liyen, Wong; Muyeba, Maybin K.; Keane, John A.; Gong, Zhiguo; Edwards-Jones, Valerie

Authors

Wong Liyen

John A. Keane

Zhiguo Gong

Valerie Edwards-Jones



Contributors

W. Liyen
Other

M.K. Muyeba
Other

J.A. Keane
Other

Z. Gong
Other

V. Edwards-Jones
Other

Abstract

The paper investigates the use of support vector machines (SVM) in classifying Matrix-Assisted Laser Desorption Ionisation (MALDI) Time Of Flight (TOF) mass spectra. MALDI-TOF screening is a simple and useful technique for rapidly identifying microorganisms and classifying them into specific subtypes. MALDI-TOF data presents data analysis challenges due to its complexity and inherent data uncertainties. In addition, there are usually large mass ranges within which to identify the spectra and this may pose problems in classification. To deal with this problem, we use Wavelets to select relevant and localized features. We then search for best optimal parameters to choose an SVM kernel and apply the SVM classifier. We compare classification accuracy and dimensionality reduction between the SVM classifier and the SVM classifier with wavelet-based feature extraction. Results show that wavelet-based feature extraction improved classification accuracy by at least 10%, feature reduction by 76% and runtime by over 80%

Presentation Conference Type Conference Paper (published)
Conference Name 9th International Conference, AMT 2013
Start Date Oct 29, 2013
End Date Oct 31, 2013
Publication Date 2013
Deposit Date Apr 1, 2025
Peer Reviewed Peer Reviewed
Pages 413-422
Series Title Lecture Notes in Computer Science
Series Number 8210
Series ISSN 1611-3349
Book Title Active Media Technology
ISBN 9783319027494
DOI https://doi.org/10.1007/978-3-319-02750-0_44