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Evolving fuzzy systems for data streams: a survey
Author(s) -
Baruah Rashmi Dutta,
Angelov Plamen
Publication year - 2011
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.42
Subject(s) - computer science , fuzzy logic , data stream mining , neuro fuzzy , data mining , artificial intelligence , fuzzy rule , computational intelligence , mode (computer interface) , fuzzy control system , architecture , data science , machine learning , human–computer interaction , art , visual arts
Evolving fuzzy systems (EFSs) can be regarded as intelligent systems based on fuzzy rule‐based or neuro‐fuzzy models with the ability to learn continuously and to gradually develop with the objective of enhancing their performance. Such systems learn in online mode by analyzing incoming samples, and adjusting both structure and parameters. The objective of this chapter is to present a brief overview of some early as well as recent EFSs by focusing on their architecture, design algorithms along with the merits and demerits, and various applications. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 461–476 DOI: 10.1002/widm.42 This article is categorized under: Technologies > Computational Intelligence