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.
This work is partially supported by Australian Research Council Discovery Projects (DP0449535, DP0559536 and DP0667060), a China NSF major research Program (60496327), China NSF grants (60463003, 10661003), an Overseas Outstanding Talent Research Program of Chinese Academy of Sciences (06S3011S01), an Overseas-Returning High-level Talent Research Program of China Hunan-Resource Ministry, and a Guangxi Postgraduate Educational Innovation Plan (2006106020812M35).
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Zhang, C., Zhu, X., Zhang, J., Qin, Y., Zhang, S. (2007). GBKII: An Imputation Method for Missing Values. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_122
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DOI: https://doi.org/10.1007/978-3-540-71701-0_122
Publisher Name: Springer, Berlin, Heidelberg
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