NDLI: A novel feature selection by clustering coefficients of variations

Content Provider	IEEE Xplore Digital Library
Author	Fong, S. Liang, J. Wong, R. Ghanavati, M.
Copyright Year	2014
Description	Author affiliation: Sch. of Comput. Sci. & Eng., Univ. of New South Wales Sydney, Sydney, NSW, Australia (Wong, R.; Ghanavati, M.) \|\| Dept. of Comput. & Inf. Sci., Univ. of Macau, Taipa, China (Fong, S.; Liang, J.)
Abstract	One of the challenges in inferring a classification model with good prediction accuracy is to select the relevant features that contribute to maximum predictive power. Many feature selection techniques have been proposed and studied in the past, but none so far claimed to be the best. In this paper, a novel and efficient feature selection method called Clustering Coefficients of Variation (CCV) is proposed. CCV is based on a very simple principle of variance-basis which finds an optimal balance between generalization and overfitting. Through a computer simulation experiment, 44 datasets with substantially large number of features are tested by CCV in comparison to four popular feature selection techniques. Results show that CCV outperformed them in all aspects of averaged performances and speed. By the simplicity of design it is anticipated that CCV will be a useful alternative of pre-processing method for classification especially with those datasets that are characterized by many features.
Starting Page	205
Ending Page	213
File Size	224355
Page Count	9
File Format	PDF
ISBN	9781479954216
DOI	10.1109/ICDIM.2014.6991429
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-29
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Feature Selection Correlation Accuracy Computational modeling Classification Predictive models Data Mining Complexity theory Standards
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Mr. Siddhartha Mukherjee siddhartha@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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