Biomedical Images Classification by Universal Nearest Neighbours Classifier Using Posterior Probability

Abstract : Universal Nearest Neighbours (unn) is a classifier recently proposed, which can also effectively estimates the posterior probability of each classification act. This algorithm, intrinsically binary, requires the use of a decomposition method to cope with multiclass problems, thus reducing their complexity in less complex binary subtasks. Then, a reconstruction rule provides the final classification. In this paper we show that the application of unn algorithm in conjunction with a reconstruction rule based on the posterior probabilities provides a classification scheme robust among different biomedical image datasets. To this aim, we compare unn performance with those achieved by Support Vector Machine with two different kernels and by a k Nearest Neighbours classifier, and applying two different reconstruction rules for each of the aforementioned classification paradigms. The results on one private and five public biomedical datasets show satisfactory performance.
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Communication dans un congrès
MICCAI - 15th International Conference on Medical Image Computing and Computer Assisted Intervention, Oct 2012, Nice, France. Springer, 7588, pp.119-127, 2012, Lecture Notes in Computer Science. <10.1007/978-3-642-35428-1_15>
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Roberto D'Ambrosio, Wafa Bel Haj Ali, Richard Nock, Paolo Soda, Franck Nielsen, et al.. Biomedical Images Classification by Universal Nearest Neighbours Classifier Using Posterior Probability. MICCAI - 15th International Conference on Medical Image Computing and Computer Assisted Intervention, Oct 2012, Nice, France. Springer, 7588, pp.119-127, 2012, Lecture Notes in Computer Science. <10.1007/978-3-642-35428-1_15>. <hal-00958909>

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