Rough neuro-fuzzy structures for classification with missing data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Imputing missing values in nuclear safeguards evaluation by a 2-tuple computational model
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Adaboost ensemble of DCOG rough-neuro-fuzzy systems
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
On classification with missing data using rough-neuro-fuzzy systems
International Journal of Applied Mathematics and Computer Science - Computational Intelligence in Modern Control Systems
Learning in rough-neuro-fuzzy system for data with missing values
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part I
| Hi-index | 0.00 |
This paper presents a new approach to fuzzy classification in the case of missing features. The rough set theory is incorporated into neuro-fuzzy structures and the rough-neurofuzzy classifier is derived. The architecture of the classifier is determined by the MICOG (modified indexed center of gravity) defuzzification method. The structure of the classifier is presented in a general form which includes both the Mamdani approach and the logical approach - based on the genuine fuzzy aplications. A theorem, which allows to determine the structures of a roughneuro-fuzzy classifiers based on the MICOG defuzzification, is given and proven. Specific rough-neuro-fuzzy structures based on the Larsen rule, the Reichenbach and the Kleene-Dienes implications are given in details. In the experiments it is shown that the classifier with the Dubois-Prade fuzzy implication is characterized by the best performance in the case of missing features