{"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/an-ensemble-classification-algorithm-based-on","title":"An Ensemble Classification Algorithm Based on Information Entropy for Data Streams","arxiv_id":"1708.03496","date":"2017-08-11","proceeding":null,"authors":["Junhong Wang","Shuliang Xu","Bingqian Duan","Caifeng Liu","Jiye Liang"],"abstract":"Data stream mining problem has caused widely concerns in the area of machine\nlearning and data mining. In some recent studies, ensemble classification has\nbeen widely used in concept drift detection, however, most of them regard\nclassification accuracy as a criterion for judging whether concept drift\nhappening or not. Information entropy is an important and effective method for\nmeasuring uncertainty. Based on the information entropy theory, a new algorithm\nusing information entropy to evaluate a classification result is developed. It\nuses ensemble classification techniques, and the weight of each classifier is\ndecided through the entropy of the result produced by an ensemble classifiers\nsystem. When the concept in data streams changing, the classifiers' weight\nbelow a threshold value will be abandoned to adapt to a new concept in one\ntime. In the experimental analysis section, six databases and four proposed\nalgorithms are executed. The results show that the proposed method can not only\nhandle concept drift effectively, but also have a better classification\naccuracy and time performance than the contrastive algorithms.","url_abs":"http://arxiv.org/abs/1708.03496v1","url_pdf":"http://arxiv.org/pdf/1708.03496v1.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":"an-ensemble-classification-algorithm-based-on","repo_url":"https://github.com/hmliangliang/ECBE-algorithm","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"drift-detection","task_name":"Drift Detection"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}