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

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type	http://eprints.org/ontology/ArticleEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #91288 Multi-Class Fuzzily Weighted Adaptive Boosting-based Self-Organising Fuzzy Inference Ensemble Systems for Classification
sameAs	Multi-Class Fuzzily Weighted Adaptive Boosting-based Self-Organising Fuzzy Inference Ensemble Systems for Classification
http://eprints.org/ontology/hasAccepted	Multi-Class Fuzzily Weighted Adaptive Boosting-based Self-Organising Fuzzy Inference Ensemble Systems for Classification (PDF)
http://eprints.org/ontology/hasDocument	Multi-Class Fuzzily Weighted Adaptive Boosting-based Self-Organising Fuzzy Inference Ensemble Systems for Classification (PDF) Multi-Class Fuzzily Weighted Adaptive Boosting-based Self-Organising Fuzzy Inference Ensemble Systems for Classification (Other) Multi-Class Fuzzily Weighted Adaptive Boosting-based Self-Organising Fuzzy Inference Ensemble Systems for Classification (Other) Multi-Class Fuzzily Weighted Adaptive Boosting-based Self-Organising Fuzzy Inference Ensemble Systems for Classification (Other) Multi-Class Fuzzily Weighted Adaptive Boosting-based Self-Organising Fuzzy Inference Ensemble Systems for Classification (Other) Multi-Class Fuzzily Weighted Adaptive Boosting-based Self-Organising Fuzzy Inference Ensemble Systems for Classification (Other)
dc:hasVersion	Multi-Class Fuzzily Weighted Adaptive Boosting-based Self-Organising Fuzzy Inference Ensemble Systems for Classification (PDF)
Title	Multi-Class Fuzzily Weighted Adaptive Boosting-based Self-Organising Fuzzy Inference Ensemble Systems for Classification
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
Date	2021-11-13
Creator	Plamen Angelov Xiaowei Gu
status	peer reviewed published
Publisher	IEEE
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.
Is Part Of	IEEE Transactions on Fuzzy Systems https://kar.kent.ac.uk/id/repository
Subject	QA Mathematics (inc Computing science)
list of authors	https://kar.kent.ac.uk/id/eprint/91288#authors
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #91288 Multi-Class Fuzzily Weighted Adaptive Boosting-based Self-Organising Fuzzy Inference Ensemble Systems for Classification

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