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A Novel Feature Selection by Clustering Coefficients of Variations
Simon Fong1; Justin Liang1; Raymond Wong2; Mojgan Ghanavati2
2014
Conference NameNinth International Conference on Digital Information Management (ICDIM 2014)
Source Publication2014 9th International Conference on Digital Information Management, ICDIM 2014
Pages205-213
Conference Date29 Sept.-1 Oct. 2014
Conference PlacePhitsanulok, Thailand
PublisherIEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
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.

KeywordClassification Data Mining Feature Selection
DOIhttps://doi.org/10.1109/ICDIM.2014.6991429
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Information Systems ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000364918800036
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Citation statistics
Cited Times [WOS]:14   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Department of Computer and Information Science, University of Macau, Taipa, Macau SAR
2.School of Computer Science and Engineering, University of New South Wales, Sydney NSW 2052, Australia
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Simon Fong,Justin Liang,Raymond Wong,et al. A Novel Feature Selection by Clustering Coefficients of Variations[C]:IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA,2014:205-213.
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