An Efficient Missing Data Imputation Model on Numerical Data

With the rising spotlight on Data Science and Big data analytics in both private and public sectors, enormous amount of domain-specific information has been collected. Missing information is a typical issue and consequently has pulled in significant consideration. Incomplete data analysis is a practical problem, and an intriguing issue. This study presents a methodology and framework for imputing missing value and at different missing rate of variables. An effective missing data imputation model based using Long Short-Term Memory (LSTM) on numerical dataset is proposed. For the hidden layers in a deep neural network, Rectified Linear Units (ReLU) is conventionally used as an activation function. However, there are situations where progressively more complicated functions are required to tackle vanishing gradient problem. The proposed LSTM with ReLU activation function is advantageous in terms of mapping features, weight distribution and loss function. We estimate LSTM-ReLU’s hyper-parameter values and compared its average RMSE performance with another neural network LSTM model LSTM-Softmax and machine learning models like Naïve Bayes and KNN. Experiments were carried out on the various datasets. Results obtained using LSTM-ReLU model outperforms LSTM-Softmax, Naïve Bayes and KNN method. LSTM-ReLU achieves an average performance gain of 40% – 46% over LSTM-Softmax imputation with varied missingness value and 40% - 42% on varied hyper-parameter size. Accuracy is much greater and mean square error less than 0.02% with Naïve Bayes and KNN model.