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Self-organizing fuzzy inference ensemble system for big streaming data classification

Gu, Xiaowei, Angelov, Plamen, Zhao, Zhijin (2021) Self-organizing fuzzy inference ensemble system for big streaming data classification. Knowledge-Based Systems, 218 . Article Number 106870. ISSN 0950-7051. (doi:10.1016/j.knosys.2021.106870) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:90374)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL:
https://doi.org/10.1016/j.knosys.2021.106870

Abstract

An evolving intelligent system (EIS) is able to self-update its system structure and meta-parameters from streaming data. However, since the majority of EISs are implemented on a single-model architecture, their performances on large-scale, complex data streams are often limited. To address this deficiency, a novel self-organizing fuzzy inference ensemble framework is proposed in this paper. As the base learner of the proposed ensemble system, the self-organizing fuzzy inference system is capable of self-learning a highly transparent predictive model from streaming data on a chunk-by-chunk basis through a human-interpretable process. Very importantly, the base learner can continuously self-adjust its decision boundaries based on the inter-class and intra-class distances between prototypes identified from successive data chunks for higher classification precision. Thanks to its parallel distributed computing architecture, the proposed ensemble framework can achieve great classification precision while maintain high computational efficiency on large-scale problems. Numerical examples based on popular benchmark big data problems demonstrate the superior performance of the proposed approach over the state-of-the-art alternatives in terms of both classification accuracy and computational efficiency.

Item Type: Article
DOI/Identification number: 10.1016/j.knosys.2021.106870
Uncontrolled keywords: Ensemble system; Evolving intelligent system; Large-scale data stream; Prototypes
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 27 Sep 2021 14:40 UTC
Last Modified: 04 Mar 2024 17:50 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90374 (The current URI for this page, for reference purposes)

University of Kent Author Information

Gu, Xiaowei.

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