Abstract
In this paper we show some preliminary results of our research in the fieldwork of classification of imbalanced datasets with SVM and stratified sampling. Our main goal is to deal with the clinical problem of automatic intestinal contractions detection in endoscopic video images. The prevalence of contractions is very low, and this yields to highly skewed training sets. Stratified sampling together with SVM have been reported in the literature to behave well in this kind of problems. We applied both the SMOTE algorithm developed by Chawla et al. and under-sampling, in a cascade system implementation to deal with the skewed training sets in the final SVM classifier. We show comparative results for both sampling techniques using precision-recall curves, which appear to be useful tools for performance testing.
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Vilariño, F., Spyridonos, P., Vitrià, J., Radeva, P. (2005). Experiments with SVM and Stratified Sampling with an Imbalanced Problem: Detection of Intestinal Contractions. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Image Analysis. ICAPR 2005. Lecture Notes in Computer Science, vol 3687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552499_86
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DOI: https://doi.org/10.1007/11552499_86
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28833-6
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