The image processing handbook (2nd ed.)
The image processing handbook (2nd ed.)
The nature of statistical learning theory
The nature of statistical learning theory
Information Retrieval
Digital Image Processing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
SVMs modeling for highly imbalanced classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
18F-FDG PET imaging analysis for computer aided Alzheimer's diagnosis
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
A pattern discovery approach to retail fraud detection
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A machine learning framework using SOMs: applications in the intestinal motility assessment
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
GSVM: An SVM for handling imbalanced accuracy between classes inbi-classification problems
Applied Soft Computing
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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.