Optimization of missing value imputation for neural networks

To train a neural network with an incomplete dataset, missing values can be replaced with plausible substitutions using missing value imputation. Various missing value imputers are available for use, each with its own competencies. Using multiple different imputers can improve the predictive performance of neural networks. Existing methods selected the best imputer or combined multiple imputers, irrespective of the training of the neural network. In this study, we propose an Optimization of Missing Value Imputation (OptMVI) method for improved training of a neural network in the presence of missing values in a training dataset. For each instance in the training dataset, multiple imputations are obtained from different imputers. A convex combination of the imputations is then used as the input for the neural network, with the combination weights indicating the relative contribution of each imputer. We simultaneously train the combination weights and neural network. This allows the combination weights to be optimized toward improving the predictive performance of the neural network. Through experimental evaluation on benchmark datasets with varying missing rates, we demonstrate that the proposed method outperforms the existing methods.