A Grey-Based Nearest Neighbor Approach for Missing Attribute Value Prediction

摘要

This paper proposes a grey-based nearest neighbor approach to predict accurately missing attribute values. First, grey relational analysis is employed to determine the nearest neighbors of an instance with missing attribute values. Accordingly, the known attribute values derived from these nearest neighbors are used to infer those missing values. Two datasets were used to demonstrate the performance of the proposed method. Experimental results show that our method outperforms both multiple imputation and mean substitution. Moreover, the proposed method was evaluated using five classification problems with incomplete data. Experimental results indicate that the accuracy of classification is maintained or even increased when the proposed method is applied for missing attribute value prediction.