Hossein Ebrahimpour-Komleh

Work place: Electronic and Computer faculty, Kashan University, Kashan, Iran

E-mail: ebrahimpour@gmail.com


Research Interests:


HosseinEbrahimpour-Komleh is currently an Assistant Professor at the Department of Electrical and Computer Engineering at the University of Kashan, Kashan, Iran. His main area of research includes Computer vision, Image Processing, Pattern Recognition, Biometrics, Robotics, Fractals, chaos theory and applications of Artificial Intelligence in Engineering. He received his Ph.D. degree in Computer engineering from Queensland University of technology, Brisbane, Australia in 2006. His Ph.D. research work was on the “Fractal Techniques for face recognition”. From 2005 to 2007 and prior to joining the University of Kashan, he was working as a Post-doc researcher in the University of Newcastle, NSW, Australia and as a visiting scientist in CSRIO Sydney. HosseinEbrahimpour-Komleh has B.Sc. and M.Sc. degrees both in computer engineering from Isfahan University of Technology (Isfahan, Iran) and Amirkabir University of Technology (Tehran, Iran,) respectively. He has served as the invited keynote speaker, editorial board member and reviewer of several journals and international and national conferences.

Author Articles
Rule Based Ensembles Using Pair Wise Neural Network Classifiers

By Moslem Mohammadi Jenghara Hossein Ebrahimpour-Komleh

DOI: https://doi.org/10.5815/ijisa.2015.04.05, Pub. Date: 8 Mar. 2015

In value estimation, the inexperienced people's estimation average is good approximation to true value, provided that the answer of these individual are independent. Classifier ensemble is the implementation of mentioned principle in classification tasks that are investigated in two aspects. In the first aspect, feature space is divided into several local regions and each region is assigned with a highly competent classifier and in the second, the base classifiers are applied in parallel and equally experienced in some ways to achieve a group consensus. In this paper combination of two methods are used. An important consideration in classifier combination is that much better results can be achieved if diverse classifiers, rather than similar classifiers, are combined. To achieve diversity in classifiers output, the symmetric pairwise weighted feature space is used and the outputs of trained classifiers over the weighted feature space are combined to inference final result. In this paper MLP classifiers are used as the base classifiers. The Experimental results show that the applied method is promising.

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