期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 36, 期 2, 页码 2794-2804出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2008.01.059
关键词
Knowledge discovery; Missing value; Random regression imputation; Deterministic regression imputation
类别
资金
- ARC [DP0667060]
- NSF [60496327, 90718020, 10661003, 60625204]
- China 973 Program [2008CB317108]
- Chinese Academy of Sciences [06S3011S01]
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.
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