HYBRID NEURO-FUZZY CLASSIFIER BASED ON NEFCLASS MODEL

Bogdan Gliwa; Aleksander Byrski

doi:10.7494/csci.2011.12.0.115

Authors

Bogdan Gliwa AGH University of Science and Technology
Aleksander Byrski AGH University of Science and Technology

DOI:

https://doi.org/10.7494/csci.2011.12.0.115

Keywords:

Neuro-fuzzy classifier, NEFCLASS, neural networks, fuzzy systems

Abstract

The paper presents hybrid neuro-fuzzy classifier, based on NEFCLASS model, which wasmodified. The presented classifier was compared to popular classifiers – neural networks andk-nearest neighbours. Efficiency of modifications in classifier was compared with methodsused in original model NEFCLASS (learning methods). Accuracy of classifier was testedusing 3 datasets from UCI Machine Learning Repository: iris, wine and breast cancer wisconsin.Moreover, influence of ensemble classification methods on classification accuracy waspresented.

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Author Biographies

Bogdan Gliwa, AGH University of Science and Technology

Faculty of Electrical Engineering, Automatics,IT and Electronics, Department of Computer Science

Aleksander Byrski, AGH University of Science and Technology

Faculty of Electrical Engineering, Automatics,IT and Electronics, Department of Computer Science

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