Abstract
This paper reports experiments that explore performance differences in two previous studies that investigated SVM classification of neonatal pain expressions using the Infant COPE database. This database contains 204 photographs of 26 neonates (age 18-36 hours) experiencing the pain of heel lancing and three nonpain stressors. In our first study, we reported experiments where representative expressions of all subjects were included in the training and testing sets, an experimental protocol suitable for intensive care situations. A second study used an experimental protocol more suitable for short-term stays: the SVMs were trained on one sample and then evaluated on an unknown sample. Whereas SVM with polynomial kernel of degree 3 obtained the best classification score (88.00%) using the first evaluation protocol, SVM with a linear kernel obtained the best classification score (82.35%) using the second protocol. However, experiments reported here indicate no significant difference in performance between linear and nonlinear kernels.
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References
Brahnam, S., Chuang, C., Shih, F.Y., Slack, M.R.: Machine Recognition and Representation of Neonate Facial Displays of Acute Pain. International Journal of Artificial Intelligence in Medicine (AIIM) (in press and available on publisher website)
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© 2006 Springer-Verlag Berlin Heidelberg
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Brahnam, S., Chuang, CF., Shih, F.Y., Slack, M.R. (2006). SVM Classification of Neonatal Facial Images of Pain. In: Bloch, I., Petrosino, A., Tettamanzi, A.G.B. (eds) Fuzzy Logic and Applications. WILF 2005. Lecture Notes in Computer Science(), vol 3849. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11676935_15
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DOI: https://doi.org/10.1007/11676935_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32529-1
Online ISBN: 978-3-540-32530-7
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