Neuro-fuzzy Rough Classifier Ensemble

Authors:
Marcin Korytkowski;Robert Nowicki;Rafał Scherer
Affiliations:
Department of Computer Engineering, Częstochowa University of Technology, Częstochowa, Poland 42-200 and Olsztyn Academy of Computer Science and Management, Olsztyn, Poland 10-165;Department of Computer Engineering, Częstochowa University of Technology, Częstochowa, Poland 42-200 and Department of Artificial Intelligence, Academy of Humanities and Economics in ...;Department of Computer Engineering, Częstochowa University of Technology, Częstochowa, Poland 42-200 and Department of Artificial Intelligence, Academy of Humanities and Economics in ...
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Year:
2009

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Abstract

The paper proposes a new ensemble of neuro-fuzzy rough set classifiers. The ensemble uses fuzzy rules derived by the Adaboost metalearning. The rules are used in an ensemble of neuro-fuzzy rough set systems to gain the ability to work with incomplete data (in terms of missing features). This feature is not common among different machine learning methods like neural networks or fuzzy systems. The systems are combined into the larger ensemble to achieve better accuracy. Simulations on a well-known benchmark showed the ability of the proposed system to perform relatively well.