A transfer weighted extreme learning machine for imbalanced classification

International Journal of Intelligent SystemsVolume 37, Issue 10 p. 7685-7705 RESEARCH ARTICLE A transfer weighted extreme learning machine for imbalanced classification Yinan Guo, Yinan Guo orcid.org/0000-0002-4276-5410 School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing, China School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, ChinaSearch for more papers by this authorBotao Jiao, Botao Jiao orcid.org/0000-0003-4856-2187 School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, ChinaSearch for more papers by this authorYing Tan, Ying Tan School of Artificial Intelligence, Key Laboratory of Machine Perceptron (MOE), Institute for Artificial Intellignce, Peking University, Beijing, ChinaSearch for more papers by this authorPei Zhang, Pei Zhang School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, ChinaSearch for more papers by this authorFengzhen Tang, Corresponding Author Fengzhen Tang tangfengzh[email protected] State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China Institute for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China Correspondence Fengzhen Tang, State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, 110016 Shenyang, China. Email: [email protected]Search for more papers by this author Yinan Guo, Yinan Guo orcid.org/0000-0002-4276-5410 School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing, China School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, ChinaSearch for more papers by this authorBotao Jiao, Botao Jiao orcid.org/0000-0003-4856-2187 School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, ChinaSearch for more papers by this authorYing Tan, Ying Tan School of Artificial Intelligence, Key Laboratory of Machine Perceptron (MOE), Institute for Artificial Intellignce, Peking University, Beijing, ChinaSearch for more papers by this authorPei Zhang, Pei Zhang School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, ChinaSearch for more papers by this authorFengzhen Tang, Corresponding Author Fengzhen Tang [email protected] State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China Institute for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, China Correspondence Fengzhen Tang, State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, 110016 Shenyang, China. Email: [email protected]Search for more papers by this author First published: 02 May 2022 https://doi.org/10.1002/int.22899 Yinan Guo and Botao Jiao are equally contributing authors. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract Previous class imbalance learning methods are mostly grounded on the assumption that all training data have been labeled, however, is impractical in many real-world applications. The limited amount of labeled instances may produce a classifier with poor generalization. To address the issue, a transfer weighted extreme learning machine (TWELM) classifier is proposed, with the purpose of extracting knowledge from other domains to improve the classification performance of a classifier in a limited labeled target domain. To be specific, a well-tuned weighted extreme learning machine classifier is first learned from source data that has been completely labeled. Subsequently, another extreme learning machine classifier is obtained from the limited labeled target domain data to preserve the target domain structural knowledge and the decision boundary information. Finally, the target classifier is optimized by minimizing the outputs of the two classifiers on unlabeled target data. Experimental results on real-world data sets show that TWELM outperforms existing algorithms on classification accuracy and computation cost. Open Research DATA AVAILABILITY STATEMENT We declare that the data that support the findings of this study are openly available in https://archive.ics.uci.edu/ml/datasets/Gas%2BSensor%2BArray%2BDrift%2BDataset. Data sharing is not applicable to this article as no new data were created or analyzed in this study. Volume37, Issue10October 2022Pages 7685-7705 RelatedInformation