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.
