A neuro-fuzzy classification technique using dynamic clustering and GSS rule generation

DOI10.1016/j.cam.2016.04.023zbMath1385.68036OpenAlexW2345004275MaRDI QIDQ313703

Heisnam Rohen Singh, Biswajit Purkayastha, Saroj Kr. Biswas

Publication date: 12 September 2016

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cam.2016.04.023

zbMATH Keywords

neuro-fuzzyfeature selectiondynamic clusteringGolden section search methodlinguistic variables

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (2)

Editorial: Mathematical modeling and computational methods ⋮ Quasi-closed elements in fuzzy posets

Cites Work

• Performance of feature-selection methods in the classification of high-dimension data
• Fuzzy sets and systems. Theory and applications
• A fuzzy neural network for pattern classification and feature selection
• Uncertainty theory. An introduction to its axiomatic foundations.
• An efficient quantum neuro-fuzzy classifier based on fuzzy entropy and compensatory operation
• Unsupervised feature selection using a neuro-fuzzy approach
• 10.1162/153244303322753616
• Fuzzy sets
• Feature subset selection by Bayesian networks: A comparison with genetic and sequential algorithms

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