Multiclass Fuzzily Weighted Adaptive-Boosting-Based Self-Organizing Fuzzy Inference Ensemble Systems for Classification

Gu, Xiaowei and Angelov, Plamen P. (2022) Multiclass Fuzzily Weighted Adaptive-Boosting-Based Self-Organizing Fuzzy Inference Ensemble Systems for Classification. IEEE Transactions on Fuzzy Systems, 30 (9). pp. 3722-3735. ISSN 1063-6706

[img]
Text (FWAdaBoostSOFIES_v1)
FWAdaBoostSOFIES_v1.pdf - Accepted Version
Available under License Creative Commons Attribution.

Download (788kB)

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 article, a novel multiclass fuzzily weighted AdaBoost (FWAdaBoost)-based ensemble system with a self-organizing fuzzy inference system (SOFIS) as the ensemble component is proposed. To better incorporate the SOFIS, FWAdaBoost utilizes the confidence scores produced by the 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:
©2022 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.
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2604
Subjects:
ID Code:
180712
Deposited By:
Deposited On:
05 Dec 2022 12:20
Refereed?:
Yes
Published?:
Published
Last Modified:
13 Dec 2022 17:10