numeric data. Our method is suitable for mixed-data classification problems faced with missing values. Moreover, our problem approach takes much less time to initialize than the most similar alternative method, Feature Weighted Grey $k$-Nearest Neighbor (FWGKNN).

The remainder of this paper is organized as follows. In Section 2, we review the KNN imputation techniques used in previous work and then provide a detailed outline of our method. We also discuss how it can be extended to the test dataset for classification and also derive the time complexity of our algorithm. In section 3, we test our proposed method against 6 standard methods in simulation settings. We evaluate our imputation method (CGKNN) in different simulation settings with classification where we artificially introduce missingness. We compare it with standard multiple imputation algorithms MICE and MissForest as well as the previous KNN based algorithms, Iterative KNNI (IKNN), Mutual Information based KNNI (MI-KNN), Grey KNNI (GKNN) and Feature-Weighted Grey KNNI (FWGKNN). In section 4, we demonstrate how our algorithm performs with classification tasks involving 3 UCI Machine Learning Repository datasets. We also check for improvement of classification accuracy after imputation of the missing data. Our method gives the best classification performance out of all 