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To reach this desired objective successfully, the modules are trained with distinct data samples and the hypotheses are combined in order to achieve an optimal prediction. To make use of boosting technique in online condition is a new approach. It motivates to meet the requirements due to its success in offline conditions. This work presents new online boosting method. We make use of mean error rate of individual base learners to achieve effective weight distribution of the instances to closely match the behavior of OzaBoost. Experimental results show that, in most of the situations, the proposed method achieves better accuracies, outperforming the other state-of-art methods.<\/jats:p>","DOI":"10.1093\/comjnl\/bxab138","type":"journal-article","created":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T11:19:10Z","timestamp":1631099950000},"page":"1-15","source":"Crossref","is-referenced-by-count":3,"title":["Mean Error Rate Weighted Online Boosting Method"],"prefix":"10.1093","volume":"66","author":[{"given":"Nagaraj","family":"Honnikoll","sequence":"first","affiliation":[{"name":"Department of Computer Science , Karnatak University, Karnataka 580003, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ishwar","family":"Baidari","sequence":"additional","affiliation":[{"name":"Department of Computer Science , Karnatak University, Karnataka 580003, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,10,5]]},"reference":[{"key":"2023011814091883300_ref1","doi-asserted-by":"crossref","first-page":"9645","DOI":"10.1109\/ACCESS.2019.2891315","article-title":"Drift-aware methodology for anomaly detection in smart grid","volume":"7","author":"Fenza","year":"2019","journal-title":"IEEE Access"},{"key":"2023011814091883300_ref2","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/978-981-13-8311-3_18","volume-title":"Intelligent Decision Technologies 2019","author":"Jedrzejowicz","year":"2020"},{"key":"2023011814091883300_ref3","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1080\/09540091.2018.1512557","article-title":"Adaptive learning on mobile network traffic data","volume":"31","author":"Liu","year":"2018","journal-title":"Connect. 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