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research-article

Double-kernelized weighted broad learning system for imbalanced data

Authors:

Zhiwen YuAuthors Info & Claims

Neural Computing and Applications, Volume 34, Issue 22

Pages 19923 - 19936

https://doi.org/10.1007/s00521-022-07534-5

Published: 01 November 2022 Publication History

Abstract

Broad learning system (BLS) is an emerging neural network with fast learning capability, which has achieved good performance in various applications. Conventional BLS does not effectively consider the problems of class imbalance. Moreover, parameter tuning in BLS requires much effort. To address the challenges mentioned above, we propose a double-kernelized weighted broad learning system (DKWBLS) to cope with imbalanced data classification. The double-kernel mapping strategy is designed to replace the random mapping mechanism in BLS, resulting in more robust features while avoiding the step of adjusting the number of nodes. Furthermore, DKWBLS considers the imbalance problem and achieves more explicit decision boundaries. Numerous experimental results show the superiority of DKWBLS in tackling imbalance problems over other imbalance learning approaches.

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Cited By

Sajid MMalik ATanveer M(2024)Intuitionistic Fuzzy Broad Learning System: Enhancing Robustness Against Noise and OutliersIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2024.340089832:8(4460-4469)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TFUZZ.2024.3400898
Jiang MZhang HLiu Y(2024)Intuitionistic fuzzy broad learning system with a new non-membership functionNeural Computing and Applications10.1007/s00521-024-10328-636:33(20699-20710)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s00521-024-10328-6

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Information & Contributors

Information

Published In

cover image Neural Computing and Applications

Neural Computing and Applications Volume 34, Issue 22

Nov 2022

1022 pages

ISSN:0941-0643

EISSN:1433-3058

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 November 2022

Accepted: 09 June 2022

Received: 21 March 2022

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Key-Area Research and Development Program of Guangdong Province
National Natural Science Foundation of China

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Cited By

Sajid MMalik ATanveer M(2024)Intuitionistic Fuzzy Broad Learning System: Enhancing Robustness Against Noise and OutliersIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2024.340089832:8(4460-4469)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TFUZZ.2024.3400898
Jiang MZhang HLiu Y(2024)Intuitionistic fuzzy broad learning system with a new non-membership functionNeural Computing and Applications10.1007/s00521-024-10328-636:33(20699-20710)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s00521-024-10328-6

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