GBKII: An Imputation Method for Missing Values

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Show simple item record Zhang, Chengqi en_US Zhu, Xiaofeng en_US Zhang, Jilian en_US Qin, Yongsong en_US Zhang, Shichao en_US
dc.contributor.editor Zhi-Hua, Zhou; Hang, Li; Qiang, Yang. en_US 2009-11-09T02:45:35Z 2009-11-09T02:45:35Z 2007 en_US
dc.identifier 2007000696 en_US
dc.identifier.citation Zhang Chengqi et al. 2007, 'GBKII: An Imputation Method for Missing Values', Springer, Berlin, Germany, pp. 1080-1087. en_US
dc.identifier.issn 978-3-540-71700-3 en_US
dc.identifier.other E1 en_US
dc.description.abstract Missing data imputation is an actual and challenging issue in machine learning and data mining. This is because missing values in a dataset can generate bias that affects the quality of the learned patterns or the classification performances. To deal with this issue, this paper proposes a Grey-Based K-NN Iteration Imputation method, called GBKII, for imputing missing values. GBKII is an instance-based imputation method, which is referred to a non-parametric regression method in statistics. It is also efficient for handling with categorical attributes. We experimentally evaluate our approach and demonstrate that GBKII is much more efficient than the k-NN and mean-substitution methods. en_US
dc.publisher Springer en_US
dc.relation.isbasedon en_US
dc.title GBKII: An Imputation Method for Missing Values en_US
dc.parent Advances in Knowledge Discovery and Data Mining en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation Berlin, Germany en_US
dc.identifier.startpage 1080 en_US
dc.identifier.endpage 1087 en_US QCIS Investment Core en_US
dc.conference Verified OK en_US
dc.conference.location Nanjing, China en_US
dc.for 080100 en_US
dc.personcode 011221 en_US
dc.personcode 0000026252 en_US
dc.personcode 0000036147 en_US
dc.personcode 0000036157 en_US
dc.personcode 020030 en_US
dc.percentage 100 en_US Artificial Intelligence and Image Processing en_US
dc.classification.type FOR-08 en_US
dc.custom Pacific-Asia Conference on Knowledge Discovery and Data Mining en_US 20070522 en_US
dc.location.activity Nanjing, China en_US
dc.staffid 020030 en_US

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