• May 15, 2019 News!Vol.7, No.5- Vol.8, No.4 has been indexed by EI (Inspec).   [Click]
  • Aug 01, 2018 News! [CFP] 2020 the annual meeting of IJMO Editorial Board, ECDMO 2020, will be held in Athens, Greece, February 15-17, 2020.   [Click]
  • Aug 05, 2019 News!Vol 9, No 5 has been published with online version. 10 original aritcles from 6 countries are published in this issue.    [Click]
General Information
    • ISSN: 2010-3697  (Online)
    • Abbreviated Title: Int. J. Model. Optim.
    • Frequency: Bimonthly
    • DOI: 10.7763/IJMO
    • Editor-in-Chief: Prof. Adrian Olaru
    • Executive Editor: Ms.Yoyo Y. Zhou
    • Abstracting/ Indexing: ProQuest, Crossref, Electronic Journals Library, Google Scholar, EI (INSPEC, IET), EBSCO, etc.
    • E-mail ijmo@iacsitp.com
Editor-in-chief
Prof. Adrian Olaru
University Politehnica of Bucharest, Romania
I'm happy to take on the position of editor in chief of IJMO. It's a journal that shows promise of becoming a recognized journal in the area of modelling and optimization. I'll work together with the editors to help it progress.
IJMO 2019 Vol.9(2): 67-71 ISSN: 2010-3697
DOI: 10.7763/IJMO.2019.V9.686

Feature Weighting Using a Clustering Approach

Mohammad Dousthagh, Mousa Nazari, Amir Mosavi, Shahaboddin Shamshirband, Anthony T. Chronopoulos
Abstract—In recent decades, the volume and size of data has significantly increased with the growth of technology. Extracting knowledge and useful patterns in high-dimensional data are challenging. In fact, unrelated features and dimensions reduce the efficiency and increase the complexity of machine learning algorithms. However, the methods used for selecting features and weighting features are a common solution for these problems. In this study, a feature weighting approach is presented based on density-based clustering. This method has been implemented in two steps. In the first step, the features were divided into clusters using density-based clustering. In the second step, the features with a higher degree of importance were selected in accordance to the target class of each cluster. In order to evaluate the efficiency, various standard datasets were classified by the feature selection and their degree of importance. The results indicated that the simplicity and suitability of the method in the high-dimensional dataset are the main advantages of the proposed method.

Index Terms—Feature selection, feature clustering; feature weighting; density-based clustering, machine learning, big data.

Mohammad Dousthagh, Mousa Nazari are with the Department of Computer Engineering, Faculty Engineering, Rouzbahan Institute of Higher Education, Sari, Iran.
Amir Mosavi is with School of the Built Environment, Oxford Brookes University, Oxford, UK, and Institute of Automation, Kando Kalman Faculty of Electrical Engineering, Obuda University, and Budapest, Hungary.
Shahaboddin Shamshirband is with the Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Viet Nam. And Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Viet Nam, S.S. is the Corresponding author. (e-mail: shahaboddin.shamshirband@tdtu.edu.vn).
Anthony T. Chronopoulos is with the Department of Computer Science, the University of Texas at San Antonio and (Visiting Faculty) Department of Computer Science, University of Patras, Greece.

[PDF]

Cite: Mohammad Dousthagh, Mousa Nazari, Amir Mosavi, Shahaboddin Shamshirband, Anthony T. Chronopoulos, "Feature Weighting Using a Clustering Approach," International Journal of Modeling and Optimization vol. 9, no. 2, pp. 67-71, 2019.

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