NIIA: Nonparametric Iterative Imputation Algorithm

Zhang, S; Jin, Z; Zhu, X

NIIA: Nonparametric Iterative Imputation Algorithm

Zhang, S Jin, Z Zhu, X

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Publisher:: Springer-Verlag
Publication Type:: Conference Proceeding
Citation:: PRICAI 2008: Trends in Artificial Intelligence, 2008, pp. 544 - 555
Issue Date:: 2008-01

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Field	Value	Language
dc.contributor.author	Zhang, S	en_US
dc.contributor.author	Jin, Z	en_US
dc.contributor.author	Zhu, X	en_US
dc.contributor.editor	Ho, TB	en_US
dc.contributor.editor	Zhou, ZH	en_US
dc.date	2008-12-15	en_US
dc.date.issued	2008-01	en_US
dc.identifier.citation	PRICAI 2008: Trends in Artificial Intelligence, 2008, pp. 544 - 555	en_US
dc.identifier.isbn	978-3-540-89196-3	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10787
dc.description.abstract	Many missing data imputation methods are based on only complete instances (instances without missing values in a dataset) when estimating plausible values for the missing values in the dataset. Actually, the information within incomplete instances (instances with missing values) can also play an important role in missing value imputation. For example, the information has been applied to identifying the neighbors of an instance with missing values in NN (nearest neighbor) imputation, and the class of the instance in clustering based imputation, where NN and clustering-based imputations are well-known efficient algorithms. Therefore, in this paper we advocate to well utilize the information within incomplete instances when estimating missing values. As an attempt, a simple and efficient nonparametric iterative imputation algorithm, called NIIA method, is designed for imputing iteratively missing target values. The NIIA method imputes each missing value several times until the algorithm converges. In the first iteration, all complete instances are used to estimate missing values. The information within incomplete instances is utilized since the second iteration. We conduct intensive experiments for evaluating the proposed approach. Our experimental results show: (1) The utilization of information within incomplete instances is of benefit to capture the distribution of a dataset much better and easier than parametric imputation. (2) NIIA method outperforms the existing methods at the accuracy, and this advantage is clearly highlighted when datasets are with high missing ratio.	en_US
dc.publisher	Springer-Verlag	en_US
dc.relation.ispartof	PRICAI 2008: Trends in Artificial Intelligence	en_US
dc.relation.ispartof	Pacific Rim International Conference on Artificial Intelligence	en_US
dc.relation.isbasedon	10.1007/978-3-540-89197-0_50	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	NIIA: Nonparametric Iterative Imputation Algorithm	en_US
dc.type	Conference Proceeding
utslib.location	Berlin	en_US
utslib.location.activity	Hanoi, Vietnam	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Hanoi, Vietnam	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 Systems, Management and Leadership
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	Berlin	en_US
pubs.start-date	2008-12-15	en_US

Abstract:

Many missing data imputation methods are based on only complete instances (instances without missing values in a dataset) when estimating plausible values for the missing values in the dataset. Actually, the information within incomplete instances (instances with missing values) can also play an important role in missing value imputation. For example, the information has been applied to identifying the neighbors of an instance with missing values in NN (nearest neighbor) imputation, and the class of the instance in clustering based imputation, where NN and clustering-based imputations are well-known efficient algorithms. Therefore, in this paper we advocate to well utilize the information within incomplete instances when estimating missing values. As an attempt, a simple and efficient nonparametric iterative imputation algorithm, called NIIA method, is designed for imputing iteratively missing target values. The NIIA method imputes each missing value several times until the algorithm converges. In the first iteration, all complete instances are used to estimate missing values. The information within incomplete instances is utilized since the second iteration. We conduct intensive experiments for evaluating the proposed approach. Our experimental results show: (1) The utilization of information within incomplete instances is of benefit to capture the distribution of a dataset much better and easier than parametric imputation. (2) NIIA method outperforms the existing methods at the accuracy, and this advantage is clearly highlighted when datasets are with high missing ratio.

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http://hdl.handle.net/10453/10787