Optimized parameters for missing data imputation

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dc.contributor.author Zhang, S
dc.contributor.author Qin, Y
dc.contributor.author Zhu, X
dc.contributor.author Zhang, J
dc.contributor.author Zhang, C
dc.date.accessioned 2009-11-09T05:35:24Z
dc.date.issued 2006
dc.identifier.citation Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006, 4099 LNAI pp. 1010 - 1016
dc.identifier.isbn 3540366679
dc.identifier.isbn 9783540366676
dc.identifier.issn 0302-9743
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/2339
dc.description.abstract To complete missing values, a solution is to use attribute correlations within data. However, it is difficult to identify such relations within data containing missing values. Accordingly, we develop a kernel-based missing data imputation method in this paper. This approach aims at making optimal statistical parameters: mean, distribution function after missing-data are imputed. We refer this approach to parameter optimization method (POP algorithm, a random regression imputation). We experimentally evaluate our approach, and demonstrate that our POP algorithm is much better than deterministic regression imputation in efficiency of generating an inference on the above two parameters. The results also show our algorithm is computationally efficient, robust and stable for the missing data imputation. © Springer-Verlag Berlin Heidelberg 2006.
dc.subject missing data, optimized paprameters, POP algorithm, Artificial Intelligence & Image Processing, 08 Information And Computing Sciences
dc.subject missing data, optimized paprameters, POP algorithm; Artificial Intelligence & Image Processing
dc.title Optimized parameters for missing data imputation
dc.type Conference Proceeding
dc.parent Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.journal.volume 4099 LNAI
dc.journal.number en_US
dc.publocation en_US
dc.publocation Japan en_US
dc.identifier.startpage 1225 en_US
dc.identifier.endpage 1232 en_US
dc.cauo.name FEIT.School of Systems, Management and Leadership en_US
dc.conference Verified OK en_US
dc.conference.location Hakodate, Japan en_US
dc.for 080109 Pattern Recognition and Data Mining
dc.for 08 Information And Computing Sciences
dc.personcode 0000020548 en_US
dc.personcode 723535 en_US
dc.percentage 100 en_US
dc.classification.name Pattern Recognition and Data Mining en_US
dc.classification.type FOR-08 en_US
dc.custom International Conference on Autonomous Agents and Multiagent Systems en_US
dc.date.activity 20060508 en_US
dc.location.activity Hakodate, Japan en_US
dc.description.keywords agents en_US
dc.staffid 723535 en_US
pubs.embargo.period Not known
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/Strength - Quantum Computation and Intelligent Systems


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