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Multi-Class Fuzzily Weighted Adaptive Boosting-based Self-Organising Fuzzy Inference Ensemble Systems for Classification

Gu, Xiaowei, Angelov, Plamen (2021) Multi-Class Fuzzily Weighted Adaptive Boosting-based Self-Organising Fuzzy Inference Ensemble Systems for Classification. IEEE Transactions on Fuzzy Systems, . ISSN 1063-6706. (doi:10.1109/TFUZZ.2021.3126116) (KAR id:91288)

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Official URL
http://dx.doi.org/10.1109/TFUZZ.2021.3126116

Abstract

Adaptive boosting (AdaBoost) is a widely used technique to construct a stronger ensemble classifier by combining a set of weaker ones. Zero-order fuzzy inference systems (FISs) are very powerful prototype-based predictive models for classification, offering both great prediction precision and high user-interpretability. However, the use of zero-order FISs as base classifiers in AdaBoost has not been explored yet. To bridge the gap, in this paper, a novel multi-class fuzzily weighted AdaBoost (FWAdaBoost)-based ensemble system with self-organising fuzzy inference system (SOFIS) as the ensemble component is proposed. To better incorporate SOFIS, FWAdaBoost utilises the confidence scores produced by SOFIS in both sample weight updating and ensemble output generation, resulting in more accurate classification boundaries and greater prediction precision. Numerical examples on a wide range of benchmark classification problems demonstrate the efficacy of the proposed approach.

Item Type: Article
DOI/Identification number: 10.1109/TFUZZ.2021.3126116
Uncontrolled keywords: AdaBoost, ensemble classifier, fuzzy inference system, multi-class classification
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Xiaowei Gu
Date Deposited: 03 Nov 2021 17:43 UTC
Last Modified: 27 Jan 2022 16:48 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/91288 (The current URI for this page, for reference purposes)
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