A fast data preprocessing procedure for support vector regression

Zhifeng, H; Wen, W; Xiaowei, Y; Jie, L; Guangquan, Z

A fast data preprocessing procedure for support vector regression

Zhifeng, H Wen, W Xiaowei, Y Jie, L

Guangquan, Z

Permalink

Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006, 4224 LNCS pp. 48 - 56
Issue Date:: 2006-01-01

Closed Access

	Filename	Description	Size
	2006004864.pdf		2.72 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	Zhifeng, H	en_US
dc.contributor.author	Wen, W	en_US
dc.contributor.author	Xiaowei, Y	en_US
dc.contributor.author	Jie, L https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Guangquan, Z	en_US
dc.date.issued	2006-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006, 4224 LNCS pp. 48 - 56	en_US
dc.identifier.isbn	3540454853	en_US
dc.identifier.isbn	9783540454854	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/1904
dc.description.abstract	A fast data preprocessing procedure (FDPP) for support vector regression (SVR) is proposed in this paper. In the presented method, the dataset is firstly divided into several subsets and then K-means clustering is implemented in each subset. The clusters are classified by their group size. The centroids with small group size are eliminated and the rest centroids are used for SVR training. The relationships between the group sizes and the noisy clusters are discussed and simulations are also given. Results show that FDPP cleans most of the noises, preserves the useful statistical information and reduces the training samples. Most importantly, FDPP runs very fast and maintains the good regression performance of SVR. © Springer-Verlag Berlin Heidelberg 2006.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A fast data preprocessing procedure for support vector regression	en_US
dc.type	Conference Proceeding
utslib.citation.volume	4224 LNCS	en_US
utslib.for	080704 Information Retrieval and Web Search	en_US
utslib.for	080605 Decision Support and Group Support Systems	en_US
dc.location.activity	BURJOS, Spain	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	4224 LNCS	en_US

Abstract:

A fast data preprocessing procedure (FDPP) for support vector regression (SVR) is proposed in this paper. In the presented method, the dataset is firstly divided into several subsets and then K-means clustering is implemented in each subset. The clusters are classified by their group size. The centroids with small group size are eliminated and the rest centroids are used for SVR training. The relationships between the group sizes and the noisy clusters are discussed and simulations are also given. Results show that FDPP cleans most of the noises, preserves the useful statistical information and reduces the training samples. Most importantly, FDPP runs very fast and maintains the good regression performance of SVR. © Springer-Verlag Berlin Heidelberg 2006.

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

http://hdl.handle.net/10453/1904