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Missing data imputation using Evolutionary k- Nearest neighbor algorithm for gene expression data

Gene expression data are recognized as a common data source which contains missing expression values. In this paper, we present a genetic algorithm optimized k- Nearest neighbor algorithm (Evolutionary kNNImputation) for missing data imputation. Despite the common imputation methods this paper addresses the effectiveness of using supervised learning algorithms for missing data imputation. We have compared the k- Nearest Neighbor Imputation algorithm with the proposed Evolutionary k- Nearest Neighbor Imputation algorithm. The two algorithms were tested using gene expression datasets. Certain percentages of values are randomly deleted in the datasets and recovered the missing values using the two algorithms. Results show that Evolutionary kNNImputation outperforms kNNImputation.