Semi-parametric optimization for missing data imputation

Qin, Y; Zhang, S; Zhu, X; Zhang, J; Zhang, C

Semi-parametric optimization for missing data imputation

Qin, Y Zhang, S Zhu, X Zhang, J Zhang, C

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Publication Type:: Journal Article
Citation:: Applied Intelligence, 2007, 27 (1), pp. 79 - 88
Issue Date:: 2007-08-01

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Field	Value	Language
dc.contributor.author	Qin, Y	en_US
dc.contributor.author	Zhang, S	en_US
dc.contributor.author	Zhu, X	en_US
dc.contributor.author	Zhang, J	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2007-08-01	en_US
dc.identifier.citation	Applied Intelligence, 2007, 27 (1), pp. 79 - 88	en_US
dc.identifier.issn	0924-669X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/9013
dc.description.abstract	Missing data imputation is an important issue in machine learning and data mining. In this paper, we propose a new and efficient imputation method for a kind of missing data: semi-parametric data. Our imputation method aims at making an optimal evaluation about Root Mean Square Error (RMSE), distribution function and quantile after missing-data are imputed. We evaluate our approaches using both simulated data and real data experimentally, and demonstrate that our stochastic semi-parametric regression imputation is much better than existing deterministic semi-parametric regression imputation in efficiency and effectiveness. © Springer Science+Business Media, LLC 2007.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0449535
dc.relation	http://purl.org/au-research/grants/arc/DP0559536
dc.relation	http://purl.org/au-research/grants/arc/DP0667060
dc.relation.ispartof	Applied Intelligence	en_US
dc.relation.isbasedon	10.1007/s10489-006-0032-0	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Semi-parametric optimization for missing data imputation	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	27	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	27	en_US

Abstract:

Missing data imputation is an important issue in machine learning and data mining. In this paper, we propose a new and efficient imputation method for a kind of missing data: semi-parametric data. Our imputation method aims at making an optimal evaluation about Root Mean Square Error (RMSE), distribution function and quantile after missing-data are imputed. We evaluate our approaches using both simulated data and real data experimentally, and demonstrate that our stochastic semi-parametric regression imputation is much better than existing deterministic semi-parametric regression imputation in efficiency and effectiveness. © Springer Science+Business Media, LLC 2007.

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http://hdl.handle.net/10453/9013