Abstract
Unsupervised learning of finite Gaussian mixture model (FGMM) is used to learn the distribution of population data. This paper proposes the use of the wild bootstrapping to create the variability of the imputed data in single missing data imputation. We compare the performance and accuracy of the proposed method in single imputation and multiple imputation from the R-package Amelia II using RMSE, R-squared, MAE and MAPE. The proposed method shows better performance when compared with the multiple imputation (MI) which is indeed known as the golden method of missing data imputation techniques.
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Appendix A: The Notation List
Appendix A: The Notation List
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Mat Jasin, A., Neagu, D., Csenki, A. (2018). The Wild Bootstrap Resampling in Regression Imputation Algorithm with a Gaussian Mixture Model. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10935. Springer, Cham. https://doi.org/10.1007/978-3-319-96133-0_17
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DOI: https://doi.org/10.1007/978-3-319-96133-0_17
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