  Missing data is a common problem in practical settings. Various imputation
methods have been developed to deal with missing data. However, even though the
label is usually available in the training data, the common practice of
imputation usually only relies on the input and ignores the label. In this
work, we illustrate how stacking the label into the input can significantly
improve the imputation of the input. In addition, we propose a classification
strategy that initializes the predicted test label with missing values and
stacks the label with the input for imputation. This allows imputing the label
and the input at the same time. Also, the technique is capable of handling data
training with missing labels without any prior imputation and is applicable to
continuous, categorical, or mixed-type data. Experiments show promising results
in terms of accuracy.
