Missing Data Imputation Methods in Classification Contexts

Abstract We examine different imputation methods that deal with missing data in classification contexts and compare the performance of the methods with an experiment study. We investigate the performance of the methods under the assumption that data are missing at random. We find that, as the number of missing holes in data increases, the imputation methods deteriorate and the misclassification rates of the imputation methods increase. We also examine the scenario where missing data are due to strategic behaviors of data providers. We find that imputation methods play an important role at deterring strategic behaviors of data providers and minimizing the misclassification rate.

Keywords missing data, imputation method, classification.

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Dynamic analyses of a flat plate and a beam subjected to a moving load

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