feature with respect to the class label in the training dataset, use it for calculating the weighted Grey distance between the instances, and then find the $k$-Nearest Neighbors of an instance with missing values. Using $k$-Nearest Neighbors, the missing value is imputed according to the weighted Grey distance. Our contributions can be summarized as follows:

1.  We use a combination of Mutual Information between each feature and the classifier variable $Y$ to weigh the Grey distance between instances in the feature matrix $X$. This metric is suited for tuning out any unnecessary features for classification and then finding the nearest neighbors relevant for imputation.

2.  We solve an imputation problem with no underlying assumptions on the structure of the feature matrix $X$ except that the data is missing completely at random (MCAR) or missing at random (MAR). Our method (CGKNN) is non-parametric in nature and does not assume any dependence between the individual features. This performs well even when the features are independent of each other.

3.  The proposed CGKNN imputation method is suited well for classification problems where the training as well as the test datasets have missing values. The feature matrix can be mixed-type, i.e., have categorical and 