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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Self-Organizing Fuzzy Belief Inference System for Classification
AU - Gu, Xiaowei
AU - Angelov, Plamen
AU - Shen, Qiang
N1 - ©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.
PY - 2022/12/31
Y1 - 2022/12/31
N2 - Evolving fuzzy systems (EFSs) are widely known as a powerful tool for streaming data prediction. In this paper, a novel zero-order EFS with a unique belief structure is proposed for data stream classification. Thanks to this new belief structure, the proposed model can handle the inter-class overlaps in a natural way and better capture the underlying multi-model structure of data streams in the form of prototypes. Utilizing data-driven soft thresholds, the proposed model self-organizes a set of prototype-based IF-THEN fuzzy belief rules from data streams for classification, and its learning outcomes are practically meaningful. With no requirement of prior knowledge in the problem domain, the proposed model is capable of self-determining the appropriate level of granularity for rule base construction, while enabling users to specify their preferences on the degree of fineness of its knowledge base. Numerical examples demonstrate the superior performance of the proposed model on a wide range of stationary and nonstationary classification benchmark problems.
AB - Evolving fuzzy systems (EFSs) are widely known as a powerful tool for streaming data prediction. In this paper, a novel zero-order EFS with a unique belief structure is proposed for data stream classification. Thanks to this new belief structure, the proposed model can handle the inter-class overlaps in a natural way and better capture the underlying multi-model structure of data streams in the form of prototypes. Utilizing data-driven soft thresholds, the proposed model self-organizes a set of prototype-based IF-THEN fuzzy belief rules from data streams for classification, and its learning outcomes are practically meaningful. With no requirement of prior knowledge in the problem domain, the proposed model is capable of self-determining the appropriate level of granularity for rule base construction, while enabling users to specify their preferences on the degree of fineness of its knowledge base. Numerical examples demonstrate the superior performance of the proposed model on a wide range of stationary and nonstationary classification benchmark problems.
KW - belief structure
KW - classification
KW - data streams
KW - evolving fuzzy systems
KW - fuzzy belief rule
UR - https://kar.kent.ac.uk/95205/1/SOFBIS_v3.pdf
U2 - 10.1109/tfuzz.2022.3179148
DO - 10.1109/tfuzz.2022.3179148
M3 - Journal article
VL - 30
SP - 5473
EP - 5483
JO - IEEE Transactions on Fuzzy Systems
JF - IEEE Transactions on Fuzzy Systems
SN - 1063-6706
IS - 12
ER -