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Once the chunk is full, its data distribution is compared with previous ones by fast local drift detection to seek potential concept drift. Taking diversity of instances and their relevance to new concept into account, multi-objective evolutionary algorithm is introduced to find the most valuable candidate instances. Among them, representative ones are randomly selected to query their ground-truth labels, and then update broad learning model for drift adaption. More especially, the number of representative is determined by the stability of adjacent historical chunks. Experimental results for 7 synthetic and 5 real-world datasets show that the proposed method outperforms five state-of-the-art ones on classification accuracy and labeling cost due to drift regions accurately identified and the labeling budget adaptively adjusted.<\/jats:p>","DOI":"10.1007\/s40747-023-01154-9","type":"journal-article","created":{"date-parts":[[2023,8,12]],"date-time":"2023-08-12T02:01:47Z","timestamp":1691805707000},"page":"899-916","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Active broad learning with multi-objective evolution for data stream classification"],"prefix":"10.1007","volume":"10","author":[{"given":"Jian","family":"Cheng","sequence":"first","affiliation":[]},{"given":"Zhiji","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Yinan","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Jiayang","family":"Pu","sequence":"additional","affiliation":[]},{"given":"Shengxiang","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,12]]},"reference":[{"issue":"12","key":"1154_CR1","doi-asserted-by":"publisher","first-page":"2346","DOI":"10.1109\/TKDE.2018.2876857","volume":"31","author":"J Lu","year":"2018","unstructured":"Lu J, Liu A, Dong F, Gu F, Gama J, Zhang G (2018) Learning under concept drift: a review. 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I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part. All the authors listed have approved the manuscript that is enclosed. In this work, with the purpose of classifying data stream under scarcity of labels, an active broad learning with multi-objective evolution is proposed. The context of the paper is organized as follows, the main research contents and contributions of this paper are briefly described in \u201cIntroduction\u201d. Some studies related to the proposed algorithm are briefly discussed in \u201cPreliminaries. \u201cProposed active broad learning with multi-objective evolution algorithm\u201d introduces the framework and key issues of MOE-ABLS.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}