ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Imbalanced SVM Learning with Margin Compensation
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
International Journal of Approximate Reasoning
Margin calibration in SVM class-imbalanced learning
Neurocomputing
Improving polyp detection algorithms for CT colonography: Pareto front approach
Pattern Recognition Letters
Lung field segmentation in digital postero-anterior chest radiographs
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Novel Fisher discriminant classifiers
Pattern Recognition
i-Vector with sparse representation classification for speaker verification
Speech Communication
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Image processing techniques have proved to be effective for the improvement of radiologists' diagnosis of lung nodules. In this paper, we present a computerized system aimed at lung nodules detection; it employs two different multi-scale schemes to identify the lung field and then extract a set of candidate regions with a high sensitivity ratio. The main focus of this work is the classification of the elements in the very unbalanced candidates set, by the use of support vector machines (SVMs). We performed several experiments with different kernels and differently balanced training sets. The results obtained show that cost-sensitive SVMs trained with very unbalanced data sets achieve promising results in terms of sensitivity and specificity.