Performance comparison between backpropagation, neuro-fuzzy network, and SVM

Authors:
Yong-Guk Kim;Min-Soo Jang;Kyoung-Sic Cho;Gwi-Tae Park
Affiliations:
School of Computer Engineering, Sejong University, Seoul, Korea;Dept. of Electrical Engineering, Korea University, Seoul, Korea;School of Computer Engineering, Sejong University, Seoul, Korea;Dept. of Electrical Engineering, Korea University, Seoul, Korea
Venue:
CSR'06 Proceedings of the First international computer science conference on Theory and Applications
Year:
2006

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Abstract

In this study, we compare the performance of well-known neural networks, namely, back-propagation (BP) algorithm, Neuro-Fuzzy network and Support Vector Machine (SVM) using the standard three database sets: Wisconsin breast cancer, Iris and wine data. Since such database have been useful for evaluating performance of a group of machine learning algorithms, a series of experiments have been carried out for three algorithms using the cross validation method. Results suggest that SVM outperforms the others and the Neuro-Fuzzy network is better than the BP algorithm for this data set.