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

Gu, Xiaowei and Angelov, Plamen (2021) Multi-Class Fuzzily Weighted Adaptive Boosting-based Self-Organizing Fuzzy Inference Ensemble Systems for Classification. IEEE Transactions on Fuzzy Systems. pp. 1-14. ISSN 1063-6706

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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:
Journal Article
Journal or Publication Title:
IEEE Transactions on Fuzzy Systems
Additional Information:
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Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2207
Subjects:
ID Code:
165407
Deposited By:
Deposited On:
01 Feb 2022 16:30
Refereed?:
Yes
Published?:
Published
Last Modified:
29 May 2022 23:33