TY - JOUR
T1 - Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification
T2 - A Survey
AU - Škrjanc, Igor
AU - Iglesias, Jose
AU - Sanchis, Araceli
AU - Leite, Daniel
AU - Lughofer, Edwin
AU - Gomide, Fernando
N1 - Funding Information:
Igor Škrjanc, Jose Iglesias and Araceli Sanchis would like to thank to the Chair of Excellence of Universidad Carlos III de Madrid, and the Bank of Santander Program for their support. Igor Škrjanc is grateful to Slovenian Research Agency with the research program P2-0219, Modeling, simulation and control. Daniel Leite acknowledges the Minas Gerais Foundation for Research and Development (FAPEMIG), process APQ-03384-18. Igor Škrjanc and Edwin Lughofer acknowledges the support by the ”LCM — K2 Center for Symbiotic Mechatronics” within the framework of the Austrian COMET-K2 program. Fernando Gomide is grateful to the Brazilian National Council for Scientific and Technological Development (CNPq) for grant 305906/2014-3.
Publisher Copyright:
© 2019
PY - 2019/7
Y1 - 2019/7
N2 - 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.
AB - 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.
KW - Adaptive systems
KW - Data streams
KW - Evolving systems
KW - Incremental learning
UR - http://www.scopus.com/inward/record.url?scp=85063734509&partnerID=8YFLogxK
U2 - 10.1016/j.ins.2019.03.060
DO - 10.1016/j.ins.2019.03.060
M3 - Article
AN - SCOPUS:85063734509
SN - 0020-0255
VL - 490
SP - 344
EP - 368
JO - Information Sciences
JF - Information Sciences
ER -