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Authors
Cheng, Jian
Zheng, Zhiji
Guo, Yinan
Pu, Jiayang
Yang, Shengxiang
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Funding
Funding for this research was provided by:
National Natural Science Foundation of China (61973305)
Key Science and Technology Innovation Project of CCTEG (No.2021-TD-ZD002)
National Key R &D Program of China (SQ2022YFB4700381)
Foundation of Key Laboratory of System Control and Information Processing, Ministry of Education, P.R. China (Scip202203)
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Article History
Received: 7 December 2022
Accepted: 16 May 2023
First Online: 12 August 2023
Declarations
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: No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all the authors for publication. 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 “Introduction”. Some studies related to the proposed algorithm are briefly discussed in “Preliminaries. “Proposed active broad learning with multi-objective evolution algorithm” introduces the framework and key issues of MOE-ABLS.
