Parimputation: From Imputation and Null-Imputation to Partially Imputation

Zhang, S

Parimputation: From Imputation and Null-Imputation to Partially Imputation

Zhang, S

Permalink

Publisher:: IEEE Computer Society
Publication Type:: Journal Article
Citation:: The IEEE Intelligent Informatics Bulletin, 2008, 9 (1), pp. 32 - 38
Issue Date:: 2008-01

Closed Access

	Filename	Description	Size
	2009005049OK.pdf		365.69 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	Zhang, S	en_US
dc.date.issued	2008-01	en_US
dc.identifier.citation	The IEEE Intelligent Informatics Bulletin, 2008, 9 (1), pp. 32 - 38	en_US
dc.identifier.issn	1727-5997	en_US
dc.identifier.uri	http://hdl.handle.net/10453/9072
dc.description.abstract	Missing data imputation is an important step in the process of machine learning and data mining when certain values are missed. Among extant imputation techniques, kNN imputation algorithm is the best one as it is a model free and efficient compared with other methods. However, the value of k must be chosen properly in using kNN imputation. In particular, when some nearest neighbors are far from a missing data, the kNN imputation algorithms are often of low efficiency. In this paper, a new imputation framework is designed. The imputation uses the left or right nearest neighbor for a missing data in a given dataset. Furthermore, a parimputation (partially imputation) strategy is proposed for dealing with the issue of missing data imputation. Specifically, some missing data are imputed when there are some complete data in a small neighborhood of the missing data and, other missing data without imputation are given up in applications, such as data mining and machine learning.	en_US
dc.publisher	IEEE Computer Society	en_US
dc.relation.ispartof	The IEEE Intelligent Informatics Bulletin	en_US
dc.rights	© 2008 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.title	Parimputation: From Imputation and Null-Imputation to Partially Imputation	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	9	en_US
utslib.for	0899 Other Information and Computing Sciences	en_US
utslib.for	08 Information and Computing Sciences	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
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.issue	1	en_US
pubs.volume	9	en_US

Abstract:

Missing data imputation is an important step in the process of machine learning and data mining when certain values are missed. Among extant imputation techniques, kNN imputation algorithm is the best one as it is a model free and efficient compared with other methods. However, the value of k must be chosen properly in using kNN imputation. In particular, when some nearest neighbors are far from a missing data, the kNN imputation algorithms are often of low efficiency. In this paper, a new imputation framework is designed. The imputation uses the left or right nearest neighbor for a missing data in a given dataset. Furthermore, a parimputation (partially imputation) strategy is proposed for dealing with the issue of missing data imputation. Specifically, some missing data are imputed when there are some complete data in a small neighborhood of the missing data and, other missing data without imputation are given up in applications, such as data mining and machine learning.

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

http://hdl.handle.net/10453/9072