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    Rights statement: This is the author’s version of a work that was accepted for publication in Information Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences, 447, 2018 DOI: 10.1016/j.ins.2018.03.004

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Self-Organising Fuzzy Logic Classifier

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Self-Organising Fuzzy Logic Classifier. / Gu, Xiaowei; Angelov, Plamen Parvanov.

In: Information Sciences, Vol. 447, 06.2018, p. 36-51.

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Gu, Xiaowei ; Angelov, Plamen Parvanov. / Self-Organising Fuzzy Logic Classifier. In: Information Sciences. 2018 ; Vol. 447. pp. 36-51.

Bibtex

@article{09bbfaf1a62846c097905115caa880f0,
title = "Self-Organising Fuzzy Logic Classifier",
abstract = "In this paper, we present a self-organising nonparametric fuzzy rule-based classifier. The proposed approach identifies prototypes from the observed data through an offline training process and uses them to build a 0-order AnYa type fuzzy rule-based system for classification. Once primed offline, it is able to continuously learn from the streaming data afterwards to follow the changing data pattern by updating the system structure and meta-parameters recursively. The meta-parameters of the proposed approach are derived from data directly. By changing the level of granularity, the proposed approach can make a trade-off between performance and computational efficiency, and, thus, the classifier is able to address a wide variety of problems with specific needs. The classifier also supports different types of distance measures. Numerical examples based on benchmark datasets demonstrate the high performance of the proposed approach and its ability of handling high-dimensional, complex, large-scale problems.",
keywords = "Classification, Fuzzy rule-based systems, Self-organising, Recursive",
author = "Xiaowei Gu and Angelov, {Plamen Parvanov}",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Information Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences, 447, 2018 DOI: 10.1016/j.ins.2018.03.004",
year = "2018",
month = jun,
doi = "10.1016/j.ins.2018.03.004",
language = "English",
volume = "447",
pages = "36--51",
journal = "Information Sciences",
issn = "0020-0255",
publisher = "Elsevier Inc.",

}

RIS

TY - JOUR

T1 - Self-Organising Fuzzy Logic Classifier

AU - Gu, Xiaowei

AU - Angelov, Plamen Parvanov

N1 - This is the author’s version of a work that was accepted for publication in Information Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences, 447, 2018 DOI: 10.1016/j.ins.2018.03.004

PY - 2018/6

Y1 - 2018/6

N2 - In this paper, we present a self-organising nonparametric fuzzy rule-based classifier. The proposed approach identifies prototypes from the observed data through an offline training process and uses them to build a 0-order AnYa type fuzzy rule-based system for classification. Once primed offline, it is able to continuously learn from the streaming data afterwards to follow the changing data pattern by updating the system structure and meta-parameters recursively. The meta-parameters of the proposed approach are derived from data directly. By changing the level of granularity, the proposed approach can make a trade-off between performance and computational efficiency, and, thus, the classifier is able to address a wide variety of problems with specific needs. The classifier also supports different types of distance measures. Numerical examples based on benchmark datasets demonstrate the high performance of the proposed approach and its ability of handling high-dimensional, complex, large-scale problems.

AB - In this paper, we present a self-organising nonparametric fuzzy rule-based classifier. The proposed approach identifies prototypes from the observed data through an offline training process and uses them to build a 0-order AnYa type fuzzy rule-based system for classification. Once primed offline, it is able to continuously learn from the streaming data afterwards to follow the changing data pattern by updating the system structure and meta-parameters recursively. The meta-parameters of the proposed approach are derived from data directly. By changing the level of granularity, the proposed approach can make a trade-off between performance and computational efficiency, and, thus, the classifier is able to address a wide variety of problems with specific needs. The classifier also supports different types of distance measures. Numerical examples based on benchmark datasets demonstrate the high performance of the proposed approach and its ability of handling high-dimensional, complex, large-scale problems.

KW - Classification

KW - Fuzzy rule-based systems

KW - Self-organising

KW - Recursive

U2 - 10.1016/j.ins.2018.03.004

DO - 10.1016/j.ins.2018.03.004

M3 - Journal article

VL - 447

SP - 36

EP - 51

JO - Information Sciences

JF - Information Sciences

SN - 0020-0255

ER -