Abstract
The data in real-world scenario often consists of missing values which leads to difficulty in analysis. Though there has been an emergence of various algorithms handling the issue, it is, in fact, troublesome to implement from industry perspective. Based on this issue, the ‘R’-software with various available packages for missing data handling can be a fruitful solution, which is hardly reported in any previous study. Hence, the availability of such packages demands analysis to compare their performances and check their suitability for a given dataset. A comparative study is performed using the ‘R’-packages, namely missForest, Multivariate imputation by chained equations (MICE), and AMELIA-II. Two classifiers, support vector machine (SVM) and logistic regression (LR), are used for prediction. The packages are compared with regard to imputation time, effects on variance, and the efficiency. The experimental results reveal that the performances depend on the dataset size and the percentage of missing values in data.
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References
Allison, P.D.: Handling missing data by maximum likelihood. In: SAS Global Forum, vol. 2012. Statistical Horizons, Havenford, PA (2012)
Batista, G.E., Monard, M.C., et al.: A study of k-nearest neighbour as an imputation method. HIS 87(251–260), 48 (2002)
Farhangfar, A., Kurgan, L.A., Pedrycz, W.: A novel framework for imputation of missing values in databases. IEEE Trans. Syst. Man Cybern.-Part A Syst. Hum. 37(5), 692–709 (2007)
Fichman, M., Cummings, J.N.: Multiple imputation for missing data: making the most of what you know. Organ. Res. Methods 6(3), 282–308 (2003)
Horton, N.J., Lipsitz, S.R.: Multiple imputation in practice: comparison of software packages for regression models with missing variables. Am. Stat. 55(3), 244–254 (2001)
Jonsson, P., Wohlin, C.: An evaluation of k-nearest neighbour imputation using likert data. In: 10th International Symposium on Software Metrics, 2004. Proceedings, pp. 108–118. IEEE (2004)
Karmaker, A., Kwek, S.: Incorporating an em-approach for handling missing attribute-values in decision tree induction. In: Fifth International Conference on Hybrid Intelligent Systems, 2005. HIS’05, pp. 6–pp. IEEE (2005)
Kumutha, V., Palaniammal, S.: An enhanced approach on handling missing values using bagging k-nn imputation. In: International Conference on Computer Communication and Informatics (ICCCI), 2013, pp. 1–8. IEEE (2013)
Malarvizhi, M.R., Thanamani, A.S.: K-nearest neighbor in missing data imputation. Int. J. Eng. Res. Dev. 5(1), 5–7 (2012)
Rubbin, D., Little, R.: Statistical analysis with missing data (1987)
Rubin, D.B.: Multiple imputation after 18+ years. J. Am. Stat. Assoc. 91(434), 473–489 (1996)
Sarkar, S., Lohani, A., Maiti, J.: Genetic algorithm-based association rule mining approach towards rule generation of occupational accidents. In: International Conference on Computational Intelligence, Communications, and Business Analytics, pp. 517–530. Springer (2017)
Sarkar, S., Vinay, S., Raj, R., Maiti, J., Mitra, P.: Application of optimized machine learning techniques for prediction of occupational accidents. Comput. Oper. Res. 106, 210–224 (2019)
Scheffer, J.: Dealing with missing data (2002)
Tsai, C.F., Chang, F.Y.: Combining instance selection for better missing value imputation. J. Syst. Softw. 122, 63–71 (2016)
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Sarkar, S., Pramanik, A., Khatedi, N., Maiti, J. (2020). An Investigation of the Effects of Missing Data Handling Using ‘R’-Packages. In: Raju, K.S., Senkerik, R., Lanka, S.P., Rajagopal, V. (eds) Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol 1079. Springer, Singapore. https://doi.org/10.1007/978-981-15-1097-7_24
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DOI: https://doi.org/10.1007/978-981-15-1097-7_24
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