Missing Data Imputation Using Socio-hawk Optimization-based Deep Neural Network

Missing data remain the common issue experienced in the real-world environment, which leads to deviation in data analysis and mining. Therefore, in order to lessen the consequences of missing data caused by human mistake, missing data imputation must be used in data processing. The traditional imputation model fails to satisfy the evaluation requirement due to its poor stability and low accuracy. Further, these models compromise the imputation accuracy of the increasing number of missing information. Hence, in this research, an optimized missing data imputation model is proposed using the Socio-hawk optimization Deep Neural Network (DNN). In this research, the DNN extracts the important features from the data, in which the missing data are estimated with an arbitrary missing pattern. It is stated that whenever the hyperparameters are tuned properly, the DNN's performance is improved. The key here is the efficient training of DNN using the suggested Socio-hawk optimization, which improves the imputation model's accuracy. To determine how well the suggested imputation model imputes missing data, it is compared to other methods. As a result, the paper's primary contribution is to effectively train DNN using the suggested Socio-hawk optimization that reduces the error rate of the imputation model. The experimental evaluation shows that the proposed missing data imputation model attains a high performance at 90%, which provides 1.0595, 1.9919, and 0.9421 of MAE, MSE, and MAPE.