{"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/hybrid-forest-a-concept-drift-aware-data","title":"Hybrid Forest: A Concept Drift Aware Data Stream Mining Algorithm","arxiv_id":"1902.03609","date":"2019-02-10","proceeding":null,"authors":["Radin Hamidi Rad","Maryam Amir Haeri"],"abstract":"Nowadays with a growing number of online controlling systems in the\norganization and also a high demand of monitoring and stats facilities that\nuses data streams to log and control their subsystems, data stream mining\nbecomes more and more vital. Hoeffding Trees (also called Very Fast Decision\nTrees a.k.a. VFDT) as a Big Data approach in dealing with the data stream for\nclassification and regression problems showed good performance in handling\nfacing challenges and making the possibility of any-time prediction. Although\nthese methods outperform other methods e.g. Artificial Neural Networks (ANN)\nand Support Vector Regression (SVR), they suffer from high latency in adapting\nwith new concepts when the statistical distribution of incoming data changes.\nIn this article, we introduced a new algorithm that can detect and handle\nconcept drift phenomenon properly. This algorithms also benefits from fast\nstartup ability which helps systems to be able to predict faster than other\nalgorithms at the beginning of data stream arrival. We also have shown that our\napproach will overperform other controversial approaches for classification and\nregression tasks.","url_abs":"http://arxiv.org/abs/1902.03609v1","url_pdf":"http://arxiv.org/pdf/1902.03609v1.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":"hybrid-forest-a-concept-drift-aware-data","repo_url":"https://github.com/radinhamidi/Hybrid_Forest","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"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}