{"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/graph-based-relational-features-for","title":"Graph Based Relational Features for Collective Classification","arxiv_id":"1702.02817","date":"2017-02-09","proceeding":null,"authors":["Immanuel Bayer","Uwe Nagel","Steffen Rendle"],"abstract":"Statistical Relational Learning (SRL) methods have shown that classification\naccuracy can be improved by integrating relations between samples. Techniques\nsuch as iterative classification or relaxation labeling achieve this by\npropagating information between related samples during the inference process.\nWhen only a few samples are labeled and connections between samples are sparse,\ncollective inference methods have shown large improvements over standard\nfeature-based ML methods. However, in contrast to feature based ML, collective\ninference methods require complex inference procedures and often depend on the\nstrong assumption of label consistency among related samples. In this paper, we\nintroduce new relational features for standard ML methods by extracting\ninformation from direct and indirect relations. We show empirically on three\nstandard benchmark datasets that our relational features yield results\ncomparable to collective inference methods. Finally we show that our proposal\noutperforms these methods when additional information is available.","url_abs":"http://arxiv.org/abs/1702.02817v1","url_pdf":"http://arxiv.org/pdf/1702.02817v1.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":"graph-based-relational-features-for","repo_url":"https://github.com/ibayer/PAKDD2015","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"relational-reasoning","task_name":"Relational Reasoning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}