IMPLEMENTATION OF MISSING VALUES HANDLING METHOD FOR EVALUATING THE SYSTEM/COMPONENT MAINTENANCE HISTORICAL DATA
DOI:
https://doi.org/10.17146/tdm.2017.19.1.3159Keywords:
missing value, data evaluation, alghorithm, implementationAbstract
Missing values are problems in data evaluation. Missing values analysis can resolve the problem of incomplete data that is not stored properly. The missing data can reduce the precision of calculation, since the amount of information is incomplete. The purpose of this study is to implement missing values handling method for systems/components maintenance historical data evaluation in RSG GAS. Statistical methods, such as listwise deletion and mean substitution, and machine learning (KNNI), were used to determine the missing data that correspond to the systems/components maintenance historical data. Mean substitution and KNNI methods were chosen since those methods do not require the formation of predictive models for each item which is experiencing missing data. Implementation of missing data analysis on systems/components maintenance data using KNNI method results in the smallest RMSE value. The result shows that KNNI method is the best method to handle missing value compared with listwise deletion or mean substitution.
References
Umathe, Vaishali H G.C. A Review on Incomplete Data And Clustering. Int. J. Comput. Sci. Inf. Technol. 2015. 6(2):1225-7.
Dong Y., Peng C.J. Principled missing data methods for researchers. Springer Plus. 2013. 2004:1-17.
https://doi.org/10.1186/2193-1801-2-222
Arumuga Nainar S A Comparative Study of Missing Value Imputation Methods on Time Series Data A Comparative Study of Missing Value Imputation Methods on Time Series Data. Int. J. Tecnol. Innov. Res. 2015. 14:1-8.
Minakshi, Rajan Vohra G. Missing Value Imputation in Multi Attribute Data Set. Int. J. Comput. Sci. Inf. Tecnol. 2014. 5(4):5315-21.
Nookhong J., Kaewrattanapat N. Efficiency Comparison of Data Mining Techniques for Missing-Value Imputation. J. Ind. Intellegent Inf. 2015. 3(4):305-9.
https://doi.org/10.12720/jiii.3.4.305-309
Schmitt P., Mandel J., Guedj M. Biometrics & Biostatistics A Comparison of Six Methods for Missing Data Imputation. J Biomet Biostat. 2015. 6(1):1-6.
https://doi.org/10.4172/2155-6180.1000224
Somasundaram R.S., Nedunchezhian R. Missing Value Imputation using Refined Mean Substitution. Int. J. Comput. Sci. 2012. 9(4):306-13.
Hartini E. Efficiency Comparison of Method of Handling Missing Value In Data Evaluation System or Component. 2016.
Arslan I.B.A. and A. A Novel Hybrid Approach to Estimating Missing Valuse in Databases Using K-Nearest Neighbors and Neural Networks. Int. J. Innov. Comput. Inf. Control. 2012. 8(7):4705-17.
Malarvizhi M.R., Thanamani A.S. K-NN Classifier Performs Better Than K-Means Clustering in Missing Value Imputation. J. Comput. Eng. 2012. 6(5):12-5.
https://doi.org/10.9790/0661-0651215
Malarvizhi M.R., Thanamani A.S. K-Nearest Neighbor in Missing Data Imputation. Int. J. Eng. Res. Dev. 2012. 5(1):5-7.
Suguna N., Thanushkodi K.G. Predicting Missing Attribute Values Using k-Means Clustering. J. Comput. Sci. 2011. 7(2):216-24.
https://doi.org/10.3844/jcssp.2011.216.224
Wael M.Khedr A.M.E. Pattern Classification for Incomplete Data Using PPCA and KNN. J. Eng. Trends Comput. Inf. Sci. 2013. 4(8):628-32
