Efficient missing data imputation for supervised learning

Abstract

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 between iterative imputation of missing values and single imputation in real-world applications. We propose an EM-style iterative imputation method, in which each missing attribute-value is iteratively filled using a predictor constructed from the known values and predicted values of the missing attribute-values from the previous iterations. Meanwhile, we demonstrate that it is reasonable to consider the imputation ordering for patching up multiple missing attribute values, and therefore introduce a method for imputation ordering. We experimentally show that our approach significantly outperforms some standard machine learning methods for handling missing values in classification tasks.