{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/application-of-ssvgmm-to-medical-data","title":"Application of SsVGMM to medical data-classification with novelty detection","arxiv_id":null,"date":"2017-07-15","proceeding":"2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2017 7","authors":["Fan Yang","Jaymar Soriano","Takatomi Kubo","Kazushi Ikeda"],"abstract":"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.","url_abs":"https://ieeexplore.ieee.org/abstract/document/8037512","url_pdf":"https://github.com/fandulu/SsVGMM/blob/master/Application_of_SsVGMM_to_Medical_Data___Classification_with_Novelty_Detection.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"application-of-ssvgmm-to-medical-data","repo_url":"https://github.com/fandulu/SsVGMM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"multi-class-classification","task_name":"Multi-class Classification"},{"task_slug":"novelty-detection","task_name":"Novelty Detection"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"},{"method_slug":"stochastic-gradient-variational-bayes","method_name":"Stochastic Gradient Variational Bayes"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}