Rough Neuro-Fuzzy Structures for Classification With Missing Data
- Resource Type
- Periodical
- Authors
- Nowicki, R.
- Source
- IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) IEEE Trans. Syst., Man, Cybern. B Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on. 39(6):1334-1347 Dec, 2009
- Subject
- Signal Processing and Analysis
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
General Topics for Engineers
Robotics and Control Systems
Power, Energy and Industry Applications
Fuzzy sets
Fuzzy systems
Rough sets
Bayesian methods
Probability
Neural networks
Decision theory
Computer science education
Artificial intelligence
Classification
fuzzy sets
missing data
neuro-fuzzy architectures
rough sets
- Language
- ISSN
- 1083-4419
1941-0492
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.