Evolving fuzzy classifiers using different model architectures

DOI10.1016/J.FSS.2008.06.019zbMath1186.68404OpenAlexW2092682490MaRDI QIDQ835288

N. E. Zubov

Publication date: 28 August 2009

Published in: Fuzzy Sets and Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.fss.2008.06.019

zbMATH Keywords

concept driftevolving Takagi-Sugeno systemsfuzzy rule-based classifiersglobal and local learningon-line and incremental learningon-line image classificationoutlier treatment

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (8)

EFNN-NullUni: an evolving fuzzy neural network based on null-uninorm ⋮ On-line incremental feature weighting in evolving fuzzy classifiers ⋮ Evolving multi-label fuzzy classifier ⋮ Recursive least mean dual p-power solution to the generalization of evolving fuzzy system under multiple noises ⋮ Jointly evolving and compressing fuzzy system for feature reduction and classification ⋮ An explainable semi-supervised self-organizing fuzzy inference system for streaming data classification ⋮ Conjecturable knowledge discovery: a fuzzy clustering approach ⋮ Autonomously evolving classifier TEDAClass

Uses Software

• ElemStatLearn
• NEFCLASS-X

Cites Work

• Unnamed Item
• Evolving rule-based models. A tool for design of flexible adaptive systems.
• An approach for fuzzy rule-base adaptation using on-line clustering
• Extensions of vector quantization for incremental clustering
• Problems of identification and control
• Fuzzy identification of systems and its applications to modeling and control
• The elements of statistical learning. Data mining, inference, and prediction

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