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- W4200320528 abstract "Abstract We compare four numerical methods for the prediction of missing values in four different datasets [1]. These methods are 1) the hierarchical maximum likelihood estimation (ℋ‐MLE), and three machine learning (ML) methods, which include 2) k‐nearest neighbors (kNN), 3) random forest, and 4) Deep Neural Network (DNN). From the ML methods, the best results (for considered datasets) were obtained by the kNN method with three (or seven) neighbors. On one dataset, the MLE method showed a smaller error than the kNN method, whereas, on another, the kNN method was better. The MLE method requires a lot of linear algebra computations and works fine on almost all datasets. Its result can be improved by taking a smaller threshold and more accurate hierarchical matrix arithmetics. To our surprise, the well‐known kNN method produces similar results as ℋ‐MLE and worked much faster." @default.
- W4200320528 created "2021-12-31" @default.
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- W4200320528 date "2021-12-01" @default.
- W4200320528 modified "2023-10-16" @default.
- W4200320528 title "Prediction of missing values. Comparison of approaches" @default.
- W4200320528 cites W2962942717 @default.
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- W4200320528 doi "https://doi.org/10.1002/pamm.202100043" @default.
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