UM  > 科技學院  > 電腦及資訊科學系
Lightweight feature selection methods based on standardized measure of dispersion for mining big data
Simon Fong; Robert P. Biuk-Aghai; Yain-Whar Si
2017-03-10
Conference NameIEEE International Conference on Computer and Information Technology (CIT)
Source Publication2016 IEEE International Conference on Computer and Information Technology (CIT)
Pages553-559
Conference Date8-10 Dec. 2016
Conference PlaceNadi, Fiji
Abstract

Big data analytics is emerging as an important research field nowadays with many technical challenges that confront both commercial IT deployment and big data research communities. One of the inherent problems of big data is the curse of dimensionality. Modern data are described with many attributes and stored with high dimensions. In data analytics, feature selection has been popularly used to lighten the processing load in inducing a data mining model. However, when mining high dimensional data the search space from which an optimal feature subset is to be derived grows exponentially. That leads to an intractable demand in computation. In order to tackle this problem of high-dimensionality and the challenge of achieving high-speed processing over big data, a collection of novel lightweight feature selection methods is proposed in this paper. The feature selection methods are designed particularly for processing high-dimensional data quickly, by fast clustering and separating attributes using the standardized measure of dispersion. For performance evaluation, several types of big data with large degrees of dimensionality are put under test with our new feature selection algorithms. The new methods achieve enhanced classification accuracy within a relatively short time in comparison to existing feature selection methods.

KeywordBig Data Classification Feature Selection
DOI10.1109/CIT.2016.120
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Information Science & Library Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Information Systems ; Information Science & Library Science
WOS IDWOS:000411239100079
Fulltext Access
Citation statistics
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
AffiliationDepartment of Computer and Information Science Faculty of Science and Technology University of Macau Taipa, Macau SAR
First Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Simon Fong,Robert P. Biuk-Aghai,Yain-Whar Si. Lightweight feature selection methods based on standardized measure of dispersion for mining big data[C],2017:553-559.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Simon Fong]'s Articles
[Robert P. Biuk-Aghai]'s Articles
[Yain-Whar Si]'s Articles
Baidu academic
Similar articles in Baidu academic
[Simon Fong]'s Articles
[Robert P. Biuk-Aghai]'s Articles
[Yain-Whar Si]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Simon Fong]'s Articles
[Robert P. Biuk-Aghai]'s Articles
[Yain-Whar Si]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.