Gu, Xiaowei (2023) Self-Adaptive Fuzzy Learning Ensemble Systems with Dimensionality Compression from Data Streams. Information Sciences, 634 . pp. 382-399. ISSN 0020-0255. (doi:10.1016/j.ins.2023.03.123) (KAR id:100539)
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Official URL: https://doi.org/10.1016/j.ins.2023.03.123 |
Abstract
Ensemble learning is a widely used methodology to build powerful predictors from multiple individual weaker ones. However, the vast majority of ensemble learning models are designed for offline application scenarios, the use of evolving fuzzy systems in ensemble learning for online learning from data streams has not been sufficiently explored, yet. In this paper, a novel self-adaptive fuzzy learning ensemble system is introduced for data stream prediction. The proposed ensemble system employs the very sparse random projection technique to compress the consequent parts of the learned fuzzy rules by individual base models to a more compressed form, thereby reducing redundant information and improving computational efficiency. To improve the overall prediction performance, a dynamical base model pruning scheme is introduced to the proposed ensemble system together with a novel inferencing scheme, such that less accurate base models will be removed from the ensemble structure at each learning cycle automatically and only these more accurate ones will be involved in joint decision-making. Numerical examples based on a wide range of benchmark datasets demonstrate the stronger prediction performance of the proposed ensemble system over the state-of-the-art alternatives.
Item Type: | Article |
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DOI/Identification number: | 10.1016/j.ins.2023.03.123 |
Uncontrolled keywords: | data stream; dimensionality compression; ensemble learning; evolving fuzzy system; prediction |
Subjects: | Q Science > QA Mathematics (inc Computing science) |
Divisions: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
Funders: | University of Kent (https://ror.org/00xkeyj56) |
Depositing User: | Xiaowei Gu |
Date Deposited: | 21 Mar 2023 14:54 UTC |
Last Modified: | 04 Jul 2023 13:52 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/100539 (The current URI for this page, for reference purposes) |
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