{"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/feature-relevance-bounds-for-ordinal","title":"Feature Relevance Bounds for Ordinal Regression","arxiv_id":"1902.07662","date":"2019-02-20","proceeding":null,"authors":["Lukas Pfannschmidt","Jonathan Jakob","Michael Biehl","Peter Tino","Barbara Hammer"],"abstract":"The increasing occurrence of ordinal data, mainly sociodemographic, led to a\nrenewed research interest in ordinal regression, i.e. the prediction of ordered\nclasses. Besides model accuracy, the interpretation of these models itself is\nof high relevance, and existing approaches therefore enforce e.g. model\nsparsity. For high dimensional or highly correlated data, however, this might\nbe misleading due to strong variable dependencies. In this contribution, we aim\nfor an identification of feature relevance bounds which - besides identifying\nall relevant features - explicitly differentiates between strongly and weakly\nrelevant features.","url_abs":"http://arxiv.org/abs/1902.07662v1","url_pdf":"http://arxiv.org/pdf/1902.07662v1.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":"feature-relevance-bounds-for-ordinal","repo_url":"https://github.com/lpfann/fri","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}