Papers › Application of SsVGMM to medical data-classification with novelty detection
Application of SsVGMM to medical data-classification with novelty detection
Fan Yang, Jaymar Soriano, Takatomi Kubo, Kazushi Ikeda
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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections