Self-Organising Fuzzy Logic Classifier

Gu, Xiaowei and Angelov, Plamen Parvanov (2018) Self-Organising Fuzzy Logic Classifier. Information Sciences, 447. pp. 36-51. ISSN 0020-0255

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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.

Item Type:
Journal Article
Journal or Publication Title:
Information Sciences
Additional Information:
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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Deposited On:
07 Mar 2018 09:14
Last Modified:
17 Sep 2023 02:08