An investigation into the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acquisition for classification problems

DOI10.1016/S0925-2312(98)00090-3zbMath0922.68096OpenAlexW2021990760MaRDI QIDQ1286503

Ilona Jagielska, Chris Matthews, Tim Whitfort

Publication date: 3 May 1999

Published in: Neurocomputing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0925-2312(98)00090-3

zbMATH Keywords

fuzzy logicrough setsneural networksgenetic algorithmsautomated knowledge acquisition

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Mining classification rules using rough sets and neural networks ⋮ Smart case-based indexing in worsted roving process: Combination of rough set and case-based reasoning ⋮ The rough set theory and applications ⋮ Approximations and reducts with covering generalized rough sets ⋮ Rough set theory for the interval-valued fuzzy information systems ⋮ Multidimensional and multideme genetic algorithms for the construction of fuzzy systems ⋮ Rough set theory: a novel approach for extraction of robust decision rules based on incremental attributes ⋮ Generalized fuzzy rough sets ⋮ Neighborhood operator systems and approximations

Uses Software

• C4.5

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