Application of SsVGMM to medical data-classification with novelty detection
There is a considerable demand to apply classification in medical analysis. A traditional classifier requires training samples from each class. However, in reality, it is possible that the testing set may include courses that are not in the training set. This inevitably causes an issue: data from an undefined class will be assigned to predefined classes. To tackle this, we propose a semi-supervised variational Gaussian mixture model to perform multi-class classification with novelty detection. Compared to some popular novelty detection methods, we demonstrate that it gets better performance on thyroid disease data, by generating the distribution of predefined classes and undefined classes, without explicitly setting a threshold.
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