TY - JOUR T1 - A Comparison of Five Methods for Missing Value Imputation in Data Sets TT - A Comparison of Five Methods for Missing Value Imputation in Data Sets AU - Cihan, Pınar PY - 2018 DA - December Y2 - 2018 JF - International Scientific and Vocational Studies Journal JO - ISVOS PB - Umut SARAY WT - DergiPark SN - 2618-5938 SP - 80 EP - 85 VL - 2 IS - 2 LA - tr AB - The missing values in the data sets do not allow for accurate analysis. Therefore, the correct imputation of missing values has become the focus of attention of researchers in recent years. This paper focuses on a comparison of most reliable and up to date estimation methods to imputing the missing values. Imputation of missing values has a very high priority because of its impact on next pre-processing, data analysis, classification, clustering, etc. Root mean square error (RMSE) value, classification accuracy and execution time are used to evaluate the performances of most popular five methods (mean, k-nearest neighbors, singular value decomposition, bayesian principal component analysis and missForest). When RMSE and classification accuracy values of methods were compared, it has observed that missForest method outperformed other methods in all datasets. KW - Missing value imputation KW - k-nearest neighbor KW - singular value decomposition KW - bayesian principal component analysis KW - missForest N2 - Themissing values in the data sets do not allow for accurate analysis. Therefore,the correct imputation of missing values has become the focus of attention ofresearchers in recent years. This paper focuses on a comparison of mostreliable and up to date estimation methods to imputing the missing values.Imputation of missing values has a very high priority because of its impact onnext pre-processing, data analysis, classification, clustering, etc. Root meansquare error (RMSE) value, classification accuracy and execution time are usedto evaluate the performances of most popular five methods (mean, k-nearestneighbors, singular value decomposition, bayesian principal component analysisand missForest). When RMSE and classification accuracy values of methods werecompared, it has observed that missForest method outperformed other methods in alldatasets. CR - [1] T.D. Pigott, “A review of methods for missing data”, Educational Resarch and Evaluation, Cilt. 7, s. 353-383. DOI: 10.1076/edre.7.4.353.8937, 2001. CR - [2] P.D. Allison, “Missing data techniques for structural equation modeling”, Journal of Abnormal Psychology, Cilt. 4, s. 545-557. 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