POP algorithm: Kernel-based imputation to treat missing values in knowledge discovery from databases

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

POP algorithm: Kernel-based imputation to treat missing values in knowledge discovery from databases

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

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Publication Type:: Journal Article
Citation:: Expert Systems with Applications, 2009, 36 (2 PART 2), pp. 2794 - 2804
Issue Date:: 2009-01-01

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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	2009-01-01	en_US
dc.identifier.citation	Expert Systems with Applications, 2009, 36 (2 PART 2), pp. 2794 - 2804	en_US
dc.identifier.issn	0957-4174	en_US
dc.identifier.uri	http://hdl.handle.net/10453/8996
dc.description.abstract	To complete missing values a solution is to use correlations between the attributes of the data. The problem is that it is difficult to identify relations within data containing missing values. Accordingly, we develop a kernel-based missing data imputation in this paper. This approach aims at making an optimal inference on statistical parameters: mean, distribution function and quantile after missing data are imputed. And we refer this approach to parameter optimization method (POP algorithm). We experimentally evaluate our approach, and demonstrate that our POP algorithm (random regression imputation) is much better than deterministic regression imputation in efficiency and generating an inference on the above parameters. © 2008 Elsevier Ltd. All rights reserved.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0667060
dc.relation.ispartof	Expert Systems with Applications	en_US
dc.relation.isbasedon	10.1016/j.eswa.2008.01.059	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	POP algorithm: Kernel-based imputation to treat missing values in knowledge discovery from databases	en_US
dc.type	Journal Article
utslib.citation.volume	2 PART 2	en_US
utslib.citation.volume	36	en_US
utslib.for	080604 Database Management	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	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	2 PART 2	en_US
pubs.publication-status	Published	en_US
pubs.volume	36	en_US

Abstract:

To complete missing values a solution is to use correlations between the attributes of the data. The problem is that it is difficult to identify relations within data containing missing values. Accordingly, we develop a kernel-based missing data imputation in this paper. This approach aims at making an optimal inference on statistical parameters: mean, distribution function and quantile after missing data are imputed. And we refer this approach to parameter optimization method (POP algorithm). We experimentally evaluate our approach, and demonstrate that our POP algorithm (random regression imputation) is much better than deterministic regression imputation in efficiency and generating an inference on the above parameters. © 2008 Elsevier Ltd. All rights reserved.

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http://hdl.handle.net/10453/8996