|
Jaume Amores, & Petia Radeva. (2005). Retrieval of IVUS Images Using Contextual Information and Elastic Matching. International Journal on Intelligent Systems, 20(5):541–560 (IF: 0.657).
|
|
|
Jaume Amores, N. Sebe, & Petia Radeva. (2006). Boosting the distance estimation: Application to the K-Nearest Neighbor Classifier. PRL - Pattern Recognition Letters, 27(3), 201–209.
|
|
|
Oriol Pujol, Petia Radeva, & Jordi Vitria. (2006). Discriminant ECOC: A Heuristic Method for Application Dependent Design of Error Correcting Output Codes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(6): 1007–1012.
|
|
|
Fernando Vilariño, Ludmila I. Kuncheva, & Petia Radeva. (2006). ROC curves and video analysis optimization in intestinal capsule endoscopy. PRL - Pattern Recognition Letters, 27(8), 875–881.
Abstract: Wireless capsule endoscopy involves inspection of hours of video material by a highly qualified professional. Time episodes corresponding to intestinal contractions, which are of interest to the physician constitute about 1% of the video. The problem is to label automatically time episodes containing contractions so that only a fraction of the video needs inspection. As the classes of contraction and non-contraction images in the video are largely imbalanced, ROC curves are used to optimize the trade-off between false positive and false negative rates. Classifier ensemble methods and simple classifiers were examined. Our results reinforce the claims from recent literature that classifier ensemble methods specifically designed for imbalanced problems have substantial advantages over simple classifiers and standard classifier ensembles. By using ROC curves with the bagging ensemble method the inspection time can be drastically reduced at the expense of a small fraction of missed contractions.
Keywords: ROC curves; Classification; Classifiers ensemble; Detection of intestinal contractions; Imbalanced classes; Wireless capsule endoscopy
|
|
|
Xavier Otazu, & Oriol Pujol. (2006). Wavelet based approach to cluster analysis. Application on low dimensional data sets. PRL - Pattern Recognition Letters, 27(14), 1590–1605.
|
|