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In this paper a real-time computer-aided biopsy (rtCAB) system is presented to support prostate cancer diagnosis. Different types of features are extracted from trans-rectal ultrasound data and an ensemble learning algorithm is used in classification phase. A new label assignment method is also employed to provide soft or crisp class labels for uncertain data. The proposed model could be implemented...
This paper addresses the supervised learning in which the class memberships of training data are subject to ambiguity. This problem is tackled in the ensemble learning and the Dempster–Shafer theory of evidence frameworks. The initial labels of the training data are ignored and by utilizing the main classes’ prototypes, each training pattern is reassigned to one class or a subset of the main classes...
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