{"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/hoeffding-trees-with-nmin-adaptation","title":"Hoeffding Trees with nmin adaptation","arxiv_id":"1808.01145","date":"2018-08-03","proceeding":null,"authors":["Eva García-Martín","Niklas Lavesson","Håkan Grahn","Emiliano Casalicchio","Veselka Boeva"],"abstract":"Machine learning software accounts for a significant amount of energy\nconsumed in data centers. These algorithms are usually optimized towards\npredictive performance, i.e. accuracy, and scalability. This is the case of\ndata stream mining algorithms. Although these algorithms are adaptive to the\nincoming data, they have fixed parameters from the beginning of the execution.\nWe have observed that having fixed parameters lead to unnecessary computations,\nthus making the algorithm energy inefficient. In this paper we present the nmin\nadaptation method for Hoeffding trees. This method adapts the value of the nmin\nparameter, which significantly affects the energy consumption of the algorithm.\nThe method reduces unnecessary computations and memory accesses, thus reducing\nthe energy, while the accuracy is only marginally affected. We experimentally\ncompared VFDT (Very Fast Decision Tree, the first Hoeffding tree algorithm) and\nCVFDT (Concept-adapting VFDT) with the VFDT-nmin (VFDT with nmin adaptation).\nThe results show that VFDT-nmin consumes up to 27% less energy than the\nstandard VFDT, and up to 92% less energy than CVFDT, trading off a few percent\nof accuracy in a few datasets.","url_abs":"http://arxiv.org/abs/1808.01145v1","url_pdf":"http://arxiv.org/pdf/1808.01145v1.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":"hoeffding-trees-with-nmin-adaptation","repo_url":"https://github.com/egarciamartin/hoeffding-nmin-adaptation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}