A new method for classification of imprecise data using fuzzy rough fuzzification

DOI10.1016/j.ins.2017.05.049zbMath1435.68327OpenAlexW2621072763MaRDI QIDQ778393

Robert K. Nowicki, Janusz T. Starczewski

Publication date: 2 July 2020

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2017.05.049

zbMATH Keywords

fuzzy setrough setimprecise datauncertainty reasoningclassifier design

Mathematics Subject Classification ID

Reasoning under uncertainty in the context of artificial intelligence (68T37)

Related Items (1)

Modifications of the Givens Training Algorithm for Artificial Neural Networks

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Nonlinear modelling and classification based on the MICOG defuzzification
• Advanced concepts in fuzzy logic and systems with membership uncertainty
• Efficient triangular type-2 fuzzy logic systems
• On fuzzy implication operators
• Fuzzy and neuro-fuzzy intelligent systems
• A comparative study of fuzzy rough sets
• Fuzzy-rough and rough-fuzzy serial combinations in neurocomputing
• Rough--fuzzy functions in classification
• Type-2 fuzzy logic: Theory and applications (Cover title: Contributions to fuzzy and rough sets theories and their applications)
• Fuzzy rough sets and multiple-premise gradual decision rules
• ROUGH FUZZY SETS AND FUZZY ROUGH SETS*
• Rough sets
• Fuzzy sets
• Centroid of a type-2-fuzzy set
• Fuzzy classifier design

This page was built for publication: A new method for classification of imprecise data using fuzzy rough fuzzification