Gu, Xiaowei (2023) A Dual-Model Semi-Supervised Self-Organizing Fuzzy Inference System for Data Stream Classification. Applied Soft Computing, 136 . Article Number 110053. ISSN 1568-4946. (doi:10.1016/j.asoc.2023.110053) (KAR id:99736)
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Official URL: https://doi.org/10.1016/j.asoc.2023.110053 |
Abstract
Semi-supervised learning from data streams is widely considered as a highly challenging task to be further researched. In this paper, a novel dual-model self-organizing fuzzy inference system composed of two recently introduced evolving fuzzy systems (EFSs) is proposed for semi-supervised learning from data streams in infinite delay environments. After being primed with a small amount of labelled data during the warm-up period, the proposed model is able to continuously self-learn and self-expand its knowledge base from unlabelled data on a chunk-by-chunk basis with minimal human expert involvement. Thanks to its dual-model structure, the proposed model combines the merits of the two EFS models such that it can continuously identify new prototypes from new pseudo-labelled data to self-improve its knowledge base whilst keeping the impact of pseudo-labelled errors on its decision-making minimized. Numerical examples based on various benchmark problems demonstrate the efficacy of the proposed method, showing its strong potential in real-world applications by offering higher classification accuracy over the state-of-the-art competitors whilst retaining high computational efficiency.
Item Type: | Article |
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DOI/Identification number: | 10.1016/j.asoc.2023.110053 |
Uncontrolled keywords: | semi-supervised learning; fuzzy inference; data stream; evolving fuzzy system |
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: | 27 Jan 2023 18:23 UTC |
Last Modified: | 22 Mar 2023 12:40 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/99736 (The current URI for this page, for reference purposes) |
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