evaluated methods. We conclude with a discussion and scope for future work in section 5.

Methodology
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In this section, we pose the missing data problem which is encountered in classification tasks. We introduce the nearest neighbor (NN) approach and the previous works done on implementing variations on the KNN imputation approach. This is followed by the concepts of mutual information (MI) and grey relational analysis (GRA) used by our method of Class-weighted Grey $k$-Nearest Neighbor (CGKNN) imputation approach. We then formalize our imputation algorithm and calculate its time complexity.

Formulation of the Problem
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Let $X = \{X_{i}\}_{i=1}^n$ be an $n \times p$-dimensional dataset of $n$ independent observations with $p$ features/attributes and $Y$ a response variable of the class labels influenced by $X$. We assume no dependence structure between the features in $X$. Let $D$ be an $n \times p$-dimensional matrix indicating the missingness of corresponding values in the dataset $X$. In practice, we obtain a random sample of size $n$ of incomplete data associated with a population $(X, Y, D)$, called the training data [@ELS09] used to train the classifier $$\label{setup}
    \mathscr{D} = \{(X_i, Y_i, D_i)\}_{i=1}^n,$$ where all the class labels in $\{Y_i\}_{i=1}^n$ are observed, $X_i = (X_{ij})_{j=1}^p 