Rough neuro-fuzzy structures for classification with missing data.
This paper presents a new approach to fuzzy classification in the case of missing data. The rough fuzzy sets are incorporated into Mamdani-type neuro-fuzzy structures, and the rough neuro-fuzzy classifier is derived. Theorems that allow the determination of the structure of a rough neuro-fuzzy classifier are given. Several experiments illustrating the performance of the rough neuro-fuzzy classifier working in the case of missing features are described.
Nowicki
Robert
R
Department of Computer Engineering, Czestochowa University of Technology, Czestochowa, Poland. rnowicki@kik.pcz.czest.pl