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Multilayer Ensemble Evolving Fuzzy Inference System

Gu, Xiaowei (2021) Multilayer Ensemble Evolving Fuzzy Inference System. IEEE Transactions on Fuzzy Systems, 29 (8). pp. 2425-2431. ISSN 1063-6706. E-ISSN 1941-0034. (doi:10.1109/TFUZZ.2020.2988846) (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:90184)

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/10.1109/TFUZZ.2020.2988846

Abstract

In order to tackle high dimensional, complex problems, learning models have to go deeper. In this article, a novel multilayer ensemble learning model with first-order evolving fuzzy systems as its building blocks is introduced. The proposed approach can effectively learn from streaming data on a sample-by-sample basis and self-organizes its multilayered system structure and meta-parameters in a feedforward, noniterative manner. Benefiting from its multilayered distributed representation learning ability, the ensemble system not only demonstrates the state-of-the-art performance on various problems, but also offers high level of system transparency and explainability. Theoretical justifications and experimental investigation show the validity and effectiveness of the proposed concept and general principles.

Item Type: Article
DOI/Identification number: 10.1109/TFUZZ.2020.2988846
Uncontrolled keywords: Mathematical model; Computational modeling; Computer architecture; Microsoft Windows; Fuzzy logic; Fuzzy systems; Neural networks; Ensemble model; evolving fuzzy system; multilayered structure; transparency
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: 13 Sep 2021 12:42 UTC
Last Modified: 14 Sep 2021 09:01 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90184 (The current URI for this page, for reference purposes)
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