classification based problems uses a feature-weighted distance metric based on Mutual Information (MI) as a measure of closeness of a feature to the class label [@garcia2009k]. The method is called Mutual Information based $k$-Nearest Neighbor (MI-KNN) Imputation. However, the distance metric used is Euclidean distance, which does not perform well with mixed-type data [@huang2006instance]. Alternatively, Grey Relational Analysis is shown to be more appropriate for capturing proximity between two instances with mixed data as well as missingness. Based on this, a Grey KNN (GKNN) imputation approach was built based on Grey distance instead of Euclidean distance and it was shown to outperform traditional KNN imputation techniques [@huang2004grey; @zhang2012nearest]. This grey distance based KNN imputation is weighted by mutual information between features (measure of inter-feature relevance) and shown to outperform IKNN, GKNN and Fuzzy $k$-Means Imputation (FKMI) [@li2004towards] in most settings, and is called the Feature Weighted Grey $k$-Nearest Neighbor (FWGKNN) method [@pan2015missing]. However, this method does not take into account each feature’s association with the class label, which is crucial when dealing with classification problems. The FWGKNN method also assumes inter-dependency of features.

We propose a Class-weighted Grey $k$-Nearest Neighbor (CGKNN) imputation method where we calculate the MI of each 