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Accelerated PSO Swarm Search Feature Selection for Data Stream Mining Big Data
Simon Fong1; Raymond Wong2; Athanasios V. Vasilakos3
2016
Source PublicationIEEE Transactions on Services Computing
ISSN1939-1374
Volume9Issue:1Pages:33
Abstract

Big Data though it is a hype up-springing many technical challenges that confront both academic research communities and commercial IT deployment, the root sources of Big Data are founded on data streams and the curse of dimensionality. It is generally known that data which are sourced from data streams accumulate continuously making traditional batch-based model induction algorithms infeasible for real-time data mining. Feature selection has been popularly used to lighten the processing load in inducing a data mining model. However, when it comes to mining over high dimensional data the search space from which an optimal feature subset is derived grows exponentially in size, leading to an intractable demand in computation. In order to tackle this problem which is mainly based on the high-dimensionality and streaming format of data feeds in Big Data, a novel lightweight feature selection is proposed. The feature selection is designed particularly for mining streaming data on the fly, by using accelerated particle swarm optimization (APSO) type of swarm search that achieves enhanced analytical accuracy within reasonable processing time. In this paper, a collection of Big Data with exceptionally large degree of dimensionality are put under test of our new feature selection algorithm for performance evaluation.

KeywordBig Data Classification Feature Selection Metaheuristics Particle Swarm Optimization Swarm Intelligence
DOIhttps://doi.org/10.1109/TSC.2015.2439695
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering
WOS IDWOS:000370748100005
PublisherIEEE COMPUTER SOC, 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA
The Source to ArticleScopus
Fulltext Access
Citation statistics
Cited Times [WOS]:73   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorSimon Fong; Raymond Wong; Athanasios V. Vasilakos
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, Kensington, NSW 2052, Australia.
3.Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology 97187, Lulea, Sweden.
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Simon Fong,Raymond Wong,Athanasios V. Vasilakos. Accelerated PSO Swarm Search Feature Selection for Data Stream Mining Big Data[J]. IEEE Transactions on Services Computing,2016,9(1):33.
APA Simon Fong,Raymond Wong,&Athanasios V. Vasilakos.(2016).Accelerated PSO Swarm Search Feature Selection for Data Stream Mining Big Data.IEEE Transactions on Services Computing,9(1),33.
MLA Simon Fong,et al."Accelerated PSO Swarm Search Feature Selection for Data Stream Mining Big Data".IEEE Transactions on Services Computing 9.1(2016):33.
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