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 p arameter op timization 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.
This work is partially supported by Australian large ARC grants (DP0449535 DP0559536 and DP0667060), a China NSFC major research Program (60496327), a China NSFC grant (60463003) and a grant from Overseas Outstanding Talent Research Program of Chinese Academy of Sciences (06S3011S01).
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Zhang, S., Qin, Y., Zhu, X., Zhang, J., Zhang, C. (2006). Optimized Parameters for Missing Data Imputation. In: Yang, Q., Webb, G. (eds) PRICAI 2006: Trends in Artificial Intelligence. PRICAI 2006. Lecture Notes in Computer Science(), vol 4099. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36668-3_124
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DOI: https://doi.org/10.1007/978-3-540-36668-3_124
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