Cost-Sensitive Imputing Missing Values with Ordering

Xiaofeng Zhu, Shichao Zhang, Jilian Zhang, Chengqi Zhang

Various approaches for dealing with missing data have been developed so far. In this paper, two strategies are proposed for cost-sensitive iterative imputing missing values with optimal ordering. Experimental results demonstrate that proposed strategies outperform the existing methods in terms of imputation cost and accuracy.

Subjects: 12. Machine Learning and Discovery; 12. Machine Learning and Discovery

Submitted: Apr 1, 2007

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