Self-Organising Fuzzy Logic Classifier

Xiaowei Gu, Plamen Parvanov Angelov

Research output: Contribution to journalArticlepeer-review

64 Citations (Scopus)
43 Downloads (Pure)

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.
Original languageEnglish
Pages (from-to)36-51
Number of pages16
JournalInformation Sciences
Volume447
Early online date06 Mar 2018
DOIs
Publication statusPublished - 01 Jun 2018
Externally publishedYes

Keywords

  • Classification
  • Fuzzy rule-based systems
  • Recursive
  • Self-organising

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