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In supervised learning, missing values usually appear in the training set. The missing values in a dataset may generate bias, affecting the quality of the supervised learning process or the performance of classification algorithms. These imply that a reliable method for dealing with missing values is necessary. In this paper, we analyze the difference(More)
The paper studies three typical weighting strategies for Shell-Neighbor Imputation (SNI) algorithm, while there are many weighting modes that can be used in the SNI. To best capture the imputation efficiency, a new metrics, called goodess, is proposed for evaluating imputation algorithms. We conduct some experiments for examining the proposed approached,(More)
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