{"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/a-user-guided-bayesian-framework-for-ensemble","title":"A User-Guided Bayesian Framework for Ensemble Feature Selection in Life Science Applications (UBayFS)","arxiv_id":"2104.14787","date":"2021-04-30","proceeding":null,"authors":["Anna Jenul","Stefan Schrunner","Jürgen Pilz","Oliver Tomic"],"abstract":"Feature selection represents a measure to reduce the complexity of high-dimensional datasets and gain insights into the systematic variation in the data. This aspect is of specific importance in domains that rely on model interpretability, such as life sciences. We propose UBayFS, an ensemble feature selection technique embedded in a Bayesian statistical framework. Our approach considers two sources of information: data and domain knowledge. We build a meta-model from an ensemble of elementary feature selectors and aggregate this information in a multinomial likelihood. The user guides UBayFS by weighting features and penalizing specific feature blocks or combinations, implemented via a Dirichlet-type prior distribution and a regularization term. In a quantitative evaluation, we demonstrate that our framework (a) allows for a balanced trade-off between user knowledge and data observations, and (b) achieves competitive performance with state-of-the-art methods.","url_abs":"https://arxiv.org/abs/2104.14787v3","url_pdf":"https://arxiv.org/pdf/2104.14787v3.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":"a-user-guided-bayesian-framework-for-ensemble","repo_url":"https://github.com/annajenul/ubayfs_experiments","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"a-user-guided-bayesian-framework-for-ensemble","repo_url":"https://github.com/annajenul/UBayFS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[{"method_slug":"feature-selection","method_name":"Feature Selection"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}