  Missing values, widely called as \textit{sparsity} in literature, is a common
characteristic of many real-world datasets. Many imputation methods have been
proposed to address this problem of data incompleteness or sparsity. However,
the accuracy of a data imputation method for a given feature or a set of
features in a dataset is highly dependent on the distribution of the feature
values and its correlation with other features. Another problem that plagues
industry deployments of machine learning (ML) solutions is concept drift
detection, which becomes more challenging in the presence of missing values.
Although data imputation and concept drift detection have been studied
extensively, little work has attempted a combined study of the two phenomena,
i.e., concept drift detection in the presence of sparsity. In this work, we
carry out a systematic study of the following: (i) different patterns of
missing values, (ii) various statistical and ML based data imputation methods
for different kinds of sparsity, (iii) several concept drift detection methods,
(iv) practical analysis of the various drift detection metrics, (v) selecting
the best concept drift detector given a dataset with missing values based on
the different metrics. We first analyze it on synthetic data and publicly
available datasets, and finally extend the findings to our deployed solution of
automated change risk assessment system. One of the major findings from our
empirical study is the absence of supremacy of any one concept drift detection
method across all the relevant metrics. Therefore, we adopt a majority voting
based ensemble of concept drift detectors for abrupt and gradual concept
drifts. Our experiments show optimal or near optimal performance can be
achieved for this ensemble method across all the metrics.
