Neuro-rough-fuzzy approach for regression modelling from missing data

DOI10.2478/v10006-012-0035-4zbMath1283.93165OpenAlexW1969981860WikidataQ57739359 ScholiaQ57739359MaRDI QIDQ5403380

Krzysztof Simiński

Publication date: 26 March 2014

Published in: International Journal of Applied Mathematics and Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2478/v10006-012-0035-4

zbMATH Keywords

clusteringimputationrough fuzzy setmissing valuesneuro-fuzzy systemmarginalisationANNBFIS

Mathematics Subject Classification ID

Fuzzy control/observation systems (93C42) Stochastic systems in control theory (general) (93E03)

Related Items (6)

A comprehensive survey on formal concept analysis, its research trends and applications ⋮ FuBiNFS -- fuzzy biclustering neuro-fuzzy system ⋮ Comparison of algorithms for clustering incomplete data ⋮ An outlier-robust neuro-fuzzy system for classification and regression ⋮ Rough subspace neuro-fuzzy system ⋮ Center-based l1–clustering method

Uses Software

• impute

Cites Work

• Impact of imputation of missing values on classification error for discrete data
• Structure identification of fuzzy model
• Fuzzy and neuro-fuzzy intelligent systems
• A new artificial neural network based fuzzy inference system with moving consequents in if-then rules and selected applications
• ROUGH FUZZY SETS AND FUZZY ROUGH SETS*
• Fuzzy identification of systems and its applications to modeling and control
• Rough sets
• An experiment in linguistic synthesis with a fuzzy logic controller
• Alternative Approaches to Missing Values in Discriminant Analysis
• Robust automatic speech recognition with missing and unreliable acoustic data
• A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters
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