GBKII: An imputation method for missing values

Publication Type:
Conference Proceeding
Citation:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2007, 4426 LNAI pp. 1080 - 1087
Issue Date:
2007-12-01
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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. © Springer-Verlag Berlin Heidelberg 2007.
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