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General Information
    • ISSN: 2010-3751
    • Frequency: Bimonthly (2012-2016); Quarterly (Since 2017)
    • DOI: 10.18178/IJFCC
    • Editor-in-Chief: Prof. Mohamed Othman
    • Executive Editor: Ms. Nancy Y. Liu
    • Abstracting/ Indexing: Google Scholar, Engineering & Technology Digital Library, and Crossref, DOAJ, Electronic Journals LibraryEI (INSPEC, IET).
    • E-mail:  ijfcc@ejournal.net 
Editor-in-chief
Prof. Mohamed Othman
Department of Communication Technology and Network Universiti Putra Malaysia, Malaysia
It is my honor to be the editor-in-chief of IJFCC. The journal publishes good papers in the field of future computer and communication. Hopefully, IJFCC will become a recognized journal among the readers in the filed of future computer and communication.
IJFCC 2017 Vol.6(3): 81-85 ISSN: 2010-3751
doi: 10.18178/ijfcc.2017.6.3.494

A Method to Clustering the Feature Ranking on Data Classification Using an Ensemble Feature Selection

Nuntawut Kaoungku, Kittisak Kerdprasop, and Nittaya Kerdprasop
Abstract—The aim of this paper is to improve the predictive performance of the classification process by means of building multiple data classification models based on the output from feature selection methods that use ensemble strategy to find the optimal set of features. Currently, the data volume has grown at an extreme rate causing a variety of problems. The big data situation has made automatic analysis tasks such as data classification facing low performance and high computational time problems when dealing with big data that are huge in both volume and dimensions. In this research work, we tackle the big data problem in the high dimensionality aspect. We propose an ensemble method to reduce data dimension by means of feature clustering to rank the potential features and also return suitable subset of features for further classifying the training data. The two paradigms of feature selection based on ensemble strategy are proposed and evaluated. Experimental results confirm the efficacy of our proposed feature ensemble method.

Index Terms—Feature selection, ensemble learning, clustering, classification.

N. Kaoungku is with the School of Computer Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand (e-mail: nuntawut@sut.ac.th).
K. Kerdprasop is with the School of Computer Engineering. He is also with Knowledge Engineering Research Unit, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand (e-mail: kerdpras@sut.ac.th).
N. Kerdprasop is with the School of Computer Engineering. She is also with Data Engineering Research Unit, Suranaree University of Technology, Nakhon Ratchasima 30000, Thailand (e-mail: nittaya@sut.ac.th).

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Cite: Nuntawut Kaoungku, Kittisak Kerdprasop, and Nittaya Kerdprasop, "A Method to Clustering the Feature Ranking on Data Classification Using an Ensemble Feature Selection," International Journal of Future Computer and Communication vol. 6, no. 3, pp. 81-85, 2017.

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