Distributed Feature Selection for Big Data Using Fuzzy Rough Sets

Kong, L; Qu, W; Yu, J; Zuo, H; Chen, G; Xiong, F; Pan, S; Lin, S; Qiu, M

Distributed Feature Selection for Big Data Using Fuzzy Rough Sets

Kong, L Qu, W Yu, J Zuo, H

Chen, G Xiong, F Pan, S

Lin, S Qiu, M

Permalink

Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Fuzzy Systems, 2020, 28, (5), pp. 846-857
Issue Date:: 2020-05-01

Closed Access

	Filename	Description	Size
	08913637.pdf	Published version	1.27 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Kong, L
dc.contributor.author	Qu, W
dc.contributor.author	Yu, J
dc.contributor.author	Zuo, H https://orcid.org/0000-0002-9122-0775
dc.contributor.author	Chen, G
dc.contributor.author	Xiong, F
dc.contributor.author	Pan, S https://orcid.org/0000-0003-0794-527X
dc.contributor.author	Lin, S
dc.contributor.author	Qiu, M
dc.date.accessioned	2020-12-18T19:51:04Z
dc.date.available	2020-12-18T19:51:04Z
dc.date.issued	2020-05-01
dc.identifier.citation	IEEE Transactions on Fuzzy Systems, 2020, 28, (5), pp. 846-857
dc.identifier.issn	1063-6706
dc.identifier.issn	1941-0034
dc.identifier.uri	http://hdl.handle.net/10453/144854
dc.description.abstract	© 1993-2012 IEEE. Fuzzy rough-set-based feature selection is an important technique for big data analysis. However, the classic fuzzy rough set algorithm takes all the data correlations into account, which leads to the centralized computing mode, requiring high computing and memory space resources. With the increasing amount of data in the big data era, the centralized server cannot afford the computation of fuzzy rough set. To enable the fuzzy rough set for big data analysis, in this article, we propose the novel distributed fuzzy rough set (DFRS)-based feature selection, which separates and assigns the tasks to multiple nodes for parallel computing. The key challenge is to maintain the global information on each distributed node without conserving the entire fuzzy relation matrix. We tackle this challenge by a dynamic data decomposition algorithm and a data summarization process on each distributed node. Extensive experiments based on multiple real datasets demonstrate that DFRS significantly improves the runtime, and its feature selection accuracy is nearly the same as the traditional centralized computing.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Fuzzy Systems
dc.relation.isbasedon	10.1109/TFUZZ.2019.2955894
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Distributed Feature Selection for Big Data Using Fuzzy Rough Sets
dc.type	Journal Article
utslib.citation.volume	28
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2020-12-18T19:50:47Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	28
utslib.citation.issue	5

Abstract:

© 1993-2012 IEEE. Fuzzy rough-set-based feature selection is an important technique for big data analysis. However, the classic fuzzy rough set algorithm takes all the data correlations into account, which leads to the centralized computing mode, requiring high computing and memory space resources. With the increasing amount of data in the big data era, the centralized server cannot afford the computation of fuzzy rough set. To enable the fuzzy rough set for big data analysis, in this article, we propose the novel distributed fuzzy rough set (DFRS)-based feature selection, which separates and assigns the tasks to multiple nodes for parallel computing. The key challenge is to maintain the global information on each distributed node without conserving the entire fuzzy relation matrix. We tackle this challenge by a dynamic data decomposition algorithm and a data summarization process on each distributed node. Extensive experiments based on multiple real datasets demonstrate that DFRS significantly improves the runtime, and its feature selection accuracy is nearly the same as the traditional centralized computing.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/144854