Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Foundations of Neuro-Fuzzy Systems
Foundations of Neuro-Fuzzy Systems
An introduction to boosting and leveraging
Advanced lectures on machine learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Two types of implications derived from uninorms
Fuzzy Sets and Systems
IEEE Transactions on Knowledge and Data Engineering
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Rough neuro-fuzzy structures for classification with missing data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy Classifier Design
On classification with missing data using rough-neuro-fuzzy systems
International Journal of Applied Mathematics and Computer Science - Computational Intelligence in Modern Control Systems
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Neural networks are able to perfectly fit to data and fuzzy logic systems use interpretable knowledge. These methods cannot handle data with missing or unknown features what can be achieved easily using rough set theory. In the paper we incorporate the rough set theory to ensembles of neuro-fuzzy systems to achieve better classification accuracy. The ensemble is created by the AdaBoost metalearning algorithm. Our approach results in accurate classification systems which can work when the number of available features is changing. Moreover, our rough-neuro-fuzzy systems use knowledge comprised in the form of fuzzy rules to perform classification. Simulations showed very clearly the accuracy of the system and the ability to work when the number of available features decreases.