Paper Info
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.
Year
DOI
Venue
2018
10.1016/j.ins.2018.03.004
Information Sciences
Keywords
Field
DocType
Classification,Fuzzy rule-based systems,Self-organising,Recursive
Fuzzy logic,Nonparametric statistics,Artificial intelligence,Granularity,Classifier (linguistics),Self organisation,Recursion,Machine learning,Mathematics,Distance measures,Fuzzy rule
Journal
Volume
ISSN
Citations 
447
0020-0255
10
PageRank 
References 
Authors
0.46
30
2
Name
Order
Citations
PageRank
Xiaowei Gu19910.96
Plamen Angelov295467.44