Comparative study of two imputation methods in R package for RGB color histogram data
DOI:
https://doi.org/10.53840/myjict4-1-59Keywords:
Missing data, Multiple imputationAbstract
Multiple imputation (MI) is a powerful tool in handling missing data issue. This paper provides a comparison of the multiple imputation method in Amelia II package and MICE package in R. Both packages are well-known and incredible to conduct the missing data research in numerous domains. There are very limited researches comparing the multiple imputation combined with other techniques in the image data context. We employ the mean absolute error (MAE) error metric to evaluate the accuracy on the predicted values based on 20% and 50% of missing data percentages. Although the implementation of MICE is time consuming, the result shows that MICE can deal with large amount of missing values while Amelia II is only capable to deal up 20% amount of missing values. Based on the MAE result, both packages show that they are superior on the particular variables.
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