A Novel Approach for Missing Value Replacement in MLP-RMSProp Based Classification Model - Suresh, G; Saraswathi, S (International Journal of Computer Sciences and Engineering, 2019)
Data Preprocessing has become a vital task to be carried out in the Data Mining process. The data becomes the most important resource due to its significance in various domains. However, it is hard to gather every data and saves it in real-time that lead to few missing data. It is not preferable to omit the missing data due to the fact that even a few amount of data acts as a significant part in the outcome. Missing value replacement acts as a main process to handle missing data prior to the prediction of hidden pattern, that exist in the dataset. This paper presents a new, Linear Regression based missing valve replacement in the MLP-RMSprop based classification model to handle missing data. Here, linear regression model is applied to predict the values to replace the missing data, which will help to improve the classification process. Then, multilayer perceptron (MLP) classifier is applied to classify the data which further tuned by the use of root mean square propagation (RMSProp) model. An extensive implementation takes place on three benchmark dataset to showcase the betterment of the presented model. The resultant values from simulation indicated that the projected model offered supreme performance over the other models.