Moslem Mohammadi Jenghara

Work place: Department of information technology, Payam Noor University, Miandoab, Iran



Research Interests:


Moslem MohammadiJenghara is Instructor of Computer Engineering at Payame Noor University (PNU),Miandoab,Iran. He is currently a PhD student in the Department of Electrical and Computer Engineering at the University of Kashan, Kashan, Iran. He had received his M.Sc. degrees in computer engineering (Artificial intelligence) in 2008, from Iran University of Science and Technology, Tehran, Iran. He has published several articles and presented several papers in international and national journals and conference about data mining, text mining and image processing. His main area of research includes Data mining, Pattern Recognition, text mining, temporal graph mining and applications of Artificial Intelligence in Bioinformatics.

Author Articles
Rule Based Ensembles Using Pair Wise Neural Network Classifiers

By Moslem Mohammadi Jenghara Hossein Ebrahimpour-Komleh

DOI:, 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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