Cost-sensitive imputing missing values with ordering

Xiaofeng, Z; Shichao, Z; Jilian, Z; Chengqi, Z

Cost-sensitive imputing missing values with ordering

Xiaofeng, Z Shichao, Z Jilian, Z Chengqi, Z

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings of the National Conference on Artificial Intelligence, 2007, 2 pp. 1922 - 1923
Issue Date:: 2007-11-28

Closed Access

	Filename	Description	Size
	2007000693OK.pdf		60.92 kB	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	Xiaofeng, Z	en_US
dc.contributor.author	Shichao, Z	en_US
dc.contributor.author	Jilian, Z	en_US
dc.contributor.author	Chengqi, Z https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2007-11-28	en_US
dc.identifier.citation	Proceedings of the National Conference on Artificial Intelligence, 2007, 2 pp. 1922 - 1923	en_US
dc.identifier.isbn	1577353234	en_US
dc.identifier.isbn	9781577353232	en_US
dc.identifier.uri	http://hdl.handle.net/10453/12619
dc.description.abstract	Various approaches for dealing with missing data have been developed so far. In this paper, two strategies are proposed for cost-sensitive iterative imputing missing values with optimal ordering. Experimental results demonstrate that proposed strategies outperform the existing methods in terms of imputation cost and accuracy. Copyright © 2007, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.	en_US
dc.relation.ispartof	Proceedings of the National Conference on Artificial Intelligence	en_US
dc.title	Cost-sensitive imputing missing values with ordering	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Vancouver, Canada	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
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	2	en_US

Abstract:

Various approaches for dealing with missing data have been developed so far. In this paper, two strategies are proposed for cost-sensitive iterative imputing missing values with optimal ordering. Experimental results demonstrate that proposed strategies outperform the existing methods in terms of imputation cost and accuracy. Copyright © 2007, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

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

http://hdl.handle.net/10453/12619