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

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Multi-Class Fuzzily Weighted Adaptive Boosting-based Self-Organizing Fuzzy Inference Ensemble Systems for Classification. / Gu, Xiaowei; Angelov, Plamen.

In: IEEE Transactions on Fuzzy Systems, 13.11.2021, p. 1-14.

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@article{7f1afddddae84a399d04dc47b3a98dea,
title = "Multi-Class Fuzzily Weighted Adaptive Boosting-based Self-Organizing Fuzzy Inference Ensemble Systems for Classification",
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.",
author = "Xiaowei Gu and Plamen Angelov",
note = "{\textcopyright}2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ",
year = "2021",
month = nov,
day = "13",
doi = "10.1109/TFUZZ.2021.3126116",
language = "English",
pages = "1--14",
journal = "IEEE Transactions on Fuzzy Systems",
issn = "1063-6706",
publisher = "IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",

}

RIS

TY - JOUR

T1 - Multi-Class Fuzzily Weighted Adaptive Boosting-based Self-Organizing Fuzzy Inference Ensemble Systems for Classification

AU - Gu, Xiaowei

AU - Angelov, Plamen

N1 - ©2021 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2021/11/13

Y1 - 2021/11/13

N2 - 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.

AB - 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.

U2 - 10.1109/TFUZZ.2021.3126116

DO - 10.1109/TFUZZ.2021.3126116

M3 - Journal article

SP - 1

EP - 14

JO - IEEE Transactions on Fuzzy Systems

JF - IEEE Transactions on Fuzzy Systems

SN - 1063-6706

ER -