Hossein Ebrahimpour-komleh

Work place: Department of Computer and Electrical Engineering, University of Kashan, Iran

E-mail: ebrahimpour@kashanu.ac.ir


Research Interests: Artificial Intelligence, Computer Vision, Pattern Recognition, Robotics, Computer Architecture and Organization, Image Processing, Analysis of Algorithms, Theory of Computation


Hossein Ebrahimpour-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. Hossein Ebrahimpour-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 editorial board member and reviewer of several journals and international and national conferences.

Author Articles
IGICA: A Hybrid Feature Selection Approach in Text Categorization

By Mohammad Mojaveriyan Hossein Ebrahimpour-komleh Seyed jalaleddin Mousavirad

DOI: https://doi.org/10.5815/ijisa.2016.03.05, Pub. Date: 8 Mar. 2016

Feature selection problem is one of the most important issues in machine learning and statistical pattern recognition. This problem is important in many applications such as text categorization because there are many redundant and irrelevant features in these applications which may reduce the classification performance. Indeed, feature selection is a method to select an appropriate subset of features for increasing the performance of learning algorithms. In the text categorization, there are many features which most of them are redundant. In this paper, a two-stage feature selection method-IGICA- based on imperialist competitive algorithm (ICA) is proposed. ICA is a new metaheuristic which is inspired by imperialist competition among countries. At the first stage of the proposed algorithm, a filtering technique using the information gain is applied and features are ranked based on their values. The top ranking features are then selected. In the second stage, ICA is applied to the select the efficient features. The presented method is evaluated on Retures-21578 dataset. The experimental results showed that the proposed method has a good ability to select efficient features compared to other methods.

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