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.

Missing value; Classification; RMSProp; Linear Regression

[1] Horton, N. J., Lipsitz, S. R., Orton, N. J. H., &Ipsitz, S. R. L. (2001). Multiple Imputation in Practice : Comparison of Software Packages for Regression Models With Missing Variables. The American Statistician, 55(3), 244–254.
[2] Batista, G. E. A. P. A., &Monard, M. C. (2002). A Study of K -Nearest Neighbour as an Imputation Method. In Soft Computing Systems: Design, Management and Applications (pp. 251–260).
[3] Malarvizhi, M. R., &Thanamani, A. S. (2012). K-Nearest Neighbor in Missing Data Imputation. International Journal of Engineering Research and Development, 5(1), 5–7.
[4] Soltanveis, F. (2016). Using Parametric Regression and KNN Algorithm With Missing Handling For Software Effort Prediction. In Artificial Intelligence and Robotics (IRANOPEN) (pp. 77–84).
[5] Chen, Q., Cho, M., Kim, M., & Wang, C. (2016). Using link-preserving imputation for logistic partially linear models with missing covariates. Computational Statistics and Data Analysis, 101, 174–185.
[6] Amiri, M., & Jensen, R. (2016). Missing data imputation using fuzzy-rough methods. Neurocomputing, 205, 152–164.
[7] Lee, K. J., & Carlin, J. B. (2010). Multiple Imputation for Missing Data : Fully Conditional Specification Versus Multivariate Normal Imputation. American Journal of Epidemiology, 171(5), 624–632.
[8] Sahri, Z., Yusof, R., &Watada, J. (2014). FINNIM : Iterative Imputation of Missing Values in Dissolved Gas Analysis Dataset. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 10(4), 2093–2102.
[9] Belanche, L. A., Kobayashi, V., &Aluja, T. (2014). Handling missing values in kernel methods with application to microbiology data. Neurocomputing, 141, 110–116.
[10] Burgette, L. F., & Reiter, J. P. (2010). Multiple Imputation for Missing Data via Sequential Regression Trees. American Journal of Epidemiology 172(9), 1070-1076.
[11] Kwon, T. Y., & Park, Y. (2015). A new multiple imputation method for bounded missing values. Statistics and Probability Letters, 107, 204–209.
[12] Deb, R., &Liew, A. W. (2016). Missing value imputation for the analysis of incomplete traffic accident data. Information Sciences, 339, 274–289.
[13] Sovilj, D., Eirola, E., Miche, Y., Björk, K., Nian, R., Akusok, A., &Lendasse, A. (2016). Extreme learning machine for missing data using multiple imputations. Neurocomputing, 174, 220–231.
[14] https://archive.ics.uci.edu/ml/datasets/Chronic_Kidney_Disease
[15] https://sci2s.ugr.es/keel/missing.php
[16] Tarkhaneh, O. and Shen, H., 2019. Training of feedforward neural networks for data classification using hybrid particle swarm optimization, Mantegna Lévy flight and neighborhood search. Heliyon, 5(4), p.e01275.
[17] Sartakhti, J.S., Zangooei, M.H. and Mozafari, K., 2012. Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA). Computer methods and programs in biomedicine, 108(2), pp. 570-579.
[18] K.D. Patel, "Review on Techniques in Natural Language Processing", International Journal of Scientific Research in Computer Science and Engineering, Vol.7, Issue.5, pp.1-4, 2019.
[19] Amin Rezaeipanah, Zahra Abshirini, Milad Boshkani Zade, "Solving University Course Timetabling Problem Using Parallel Genetic Algorithm", International Journal of Scientific Research in Computer Science and Engineering, Vol.7, Issue.5, pp.5-13, 2019.