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A Dual-Model Semi-Supervised Self-Organizing Fuzzy Inference System for Data Stream Classification

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)

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
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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