Paper Info
Title
Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: A Survey.
Abstract
Major assumptions in computational intelligence and machine learning consist of the availability of a historical dataset for model development, and that the resulting model will, to some extent, handle similar instances during its online operation. However, in many real-world applications, these assumptions may not hold as the amount of previously available data may be insufficient to represent the underlying system, and the environment and the system may change over time. As the amount of data increases, it is no longer feasible to process data efficiently using iterative algorithms, which typically require multiple passes over the same portions of data. Evolving modeling from data streams has emerged as a framework to address these issues properly by self-adaptation, single-pass learning steps and evolution as well as contraction of model components on demand and on the fly. This survey focuses on evolving fuzzy rule-based models and neuro-fuzzy networks for clustering, classification and regression and system identification in online, real-time environments where learning and model development should be performed incrementally.
Year
DOI
Venue
2019
10.1016/j.ins.2019.03.060
Information Sciences
Keywords
Field
DocType
Evolving systems,Incremental learning,Adaptive systems,Data streams
Neuro-fuzzy,Data stream mining,Regression,Computational intelligence,Fuzzy logic,Artificial intelligence,System identification,Cluster analysis,Machine learning,Mathematics,Fuzzy rule
Journal
Volume
ISSN
Citations 
490
0020-0255
36
PageRank 
References 
Authors
1.03
0
6
Name
Order
Citations
PageRank
Igor Skrjanc135452.47
José Antonio Iglesias226120.54
Araceli Sanchis335740.26
Daniel Leite4532.05
Edwin Lughofer5194099.72
Fernando Gomide663149.76