Work place: Amirkabir University of Technology, Iran
Research Interests: Computer systems and computational processes, Neural Networks, Pattern Recognition, Computer Architecture and Organization, Computer Networks, Image Processing
Karim Faez Was born in Semnan, Iran. He received his BSc. degree in Electrical Engineering from Tehran Polytechnic University as the first rank in June 1973, and his MSc. and Ph.D. degrees in Computer Science from University of California at Los Angeles (UCLA) in 1977 and 1980 respectively. Professor Faez was with Iran Telecommunication Research Center (1981-1983) before Joining Amirkabir University of Technology (Tehran Polytechnic) in Iran in March 1983, where he holds the rank of Professor in the Electrical Engineering Department. He was the founder of the Computer Engineering Department of Amirkabir University in 1989 and he has served as the first chairman during April 1989-Sept. 1992. Professor Faez was the chairman of planning committee for Computer Engineering and Computer Science of Ministry of Science, Research and Technology (during 1988-1996). His research interests are in Biometrics Recognition and authentication, Pattern Recognition, Image Processing, Neural Networks, Signal Processing, Farsi Handwritten Processing, Earthquake Signal Processing, Fault Tolerance System Design, Computer Networks, and Hardware Design. Dr. Faez coauthored a book in Logic Circuits published by Amirkabir University Press.
DOI: https://doi.org/10.5815/ijmecs.2014.11.02, Pub. Date: 8 Nov. 2014
In this paper, a hybrid classifier using fuzzy clustering and several neural networks has been proposed. With using the fuzzy C-means algorithm, training samples will be clustered and the inappropriate data will be detected and moved to another dataset (Removed-Dataset) and used differently in the classification phase. Also, in the proposed method using the membership degree of samples to the clusters, the class of samples will be changed to the fuzzy class. Thus, for example in KDD cup99 dataset, any sample will have 5 membership degrees to classes DoS, Probe, Normal, U2R, and R2L. Afterwards, the neural networks will be trained by new labels then using a combination of regression and classification methods, the hybrid classifier will be created. Also to classify the outlier data, a fuzzy ARTMAP neural network is employed which is a part of the hybrid classifier.
Evaluation of the proposed method is performed by KDDCup99 dataset for intrusion detection and Cambridge datasets for traffic classification problems. Our experimental results indicate that the proposed system has performed better than the previous works in the case of precision, recall and f-value also detection and false alarm rate. Also, ROC curve analysis shows that the proposed hybrid classifier has been better than the famous non-hybrid classifiers.
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