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POP algorithm: Kernel-based imputation to treat missing values in knowledge discovery from databases
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DOI：10.1016/j.eswa.2008.01.059.png)
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Abstract
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To complete missing values a solution is to use correlations between the attributes of the data. The problem is that it is difficult to identify relations within data containing missing values. Accordingly, we develop a kernel-based missing data imputation in this paper. This approach aims at making an optimal inference oil statistical parameters: mean, distribution function and quantile after missing data are imputed. And we refer this approach to parameter optimization method (POP algorithm). We experimentally evaluate our approach, and demonstrate that our POP algorithm (random regression imputation) is much better than deterministic regression imputation in efficiency and generating an inference on the above parameters. (C) 2008 Elsevier Ltd. All rights reserved.
Keywords:
Knowledge discovery
Missing value
Random regression imputation
Deterministic regression imputation
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