Hybrid Methods for Robust Tabular Data Imputation
Organizations: Department of Data Science Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen, Germany
Abstract
Missing data are a fundamental challenge in statistical analysis and machine learning, as the choice of imputation method substantially impacts downstream inference. In this work, we propose two hybrid imputation methods called NuclearForest and SoftForest, which combine nuclear-norm-based low-rank initialization using Singular Value Thresholding (SVT) and SoftImpute, respectively, with a non-iterative Random Forest refinement. For the SVT-based component, we further introduce an adaptive step-size rule, prove adaptive step-size bounds, and establish convergence for the corresponding zero-initialized iteration. The low-rank initialization provides a structured warm start that captures the global covariance patterns in the data, while the subsequent Random Forest step recovers residual nonlinear signals encoding local dependencies. We conduct an extensive benchmark on diverse datasets from different application domains, comparing the proposed methods with seven established imputation methods under the Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR) mechanisms across varying missingness rates. Our results demonstrate that NuclearForest and SoftForest match or exceed the imputation fidelity of state-of-the-art iterative methods such as MissForest, while significantly reducing computational cost. In particular, they achieve speedups of approximately 5.81 times and 9.52 times over MissForest by replacing iterative cycles with a single refinement step. Our approach effectively exploits the low-rank structure of real-world tabular data and accommodates mixed-type variables, providing an efficient and robust solution for data imputation in bioinformatics, economics, and beyond.
Figures & tables
| Mechanism | MissForest | SoftForest | NuclearForest |
|---|---|---|---|
| MCAR/MAR | 77.787 ± 21.426 | 8.171 ± 1.931 | 13.389 ± 2.365 |
| MNAR | 89.438 ± 22.845 | 4.344 ± 2.137 | 9.797 ± 2.381 |
| Mechanism | MissForest | SoftForest | NuclearForest |
|---|---|---|---|
| MCAR | 5.934 ± 0.187 | 0.664 ± 0.045 | 0.659 ± 0.047 |
| MAR | 5.973 ± 0.198 | 0.661 ± 0.039 | 0.661 ± 0.034 |
Appendix figures & tables45 assets
Supplementary material from the paper’s appendix.
Appendix
| Method | Configuration / hyperparameters |
|---|---|
| Mean | Column-wise mean imputation. |
| Median | Column-wise median imputation. |
| Half-min | Missing entries are replaced by one half of the observed column minimum when the minimum is positive; otherwise a floor value of is used. |
| kNN | nearest neighbors with distance-based weighting. |
| MissForest | Iterative Random Forest imputation using 100 trees, maximum iterations , median initialization, and n_jobs=-1 . |
| SVT_OGpaper | The original SVT method with a fixed step size and , zero initialization, skip-ahead dual initialization, maximum iterations , and tolerance . |
| Setting | Value |
|---|---|
| Number of repeated runs | 10 |
| Random seeds | , assigned sequentially to the 10 repeated runs |
| Missingness rates |
| Experiment | Total runtime (min) | Total runtime (h) |
|---|---|---|
| Metabolomics dataset under MCAR/MAR missingness | 192.5 | 3.21 |
| Metabolomics dataset under MNAR missingness | 192.0 | 3.20 |
| Housing dataset under MCAR missingness | 12.9 | 0.22 |
| Housing dataset under MAR missingness | 13.0 | 0.22 |
| Rate | SVT_OGpaper | SVT_partial | SVT |
|---|---|---|---|
| 10% | 0.806 ± 0.011 | 0.804 ± 0.010 | 0.740 ± 0.050 |
| 20% | 0.920 ± 0.029 | 0.917 ± 0.028 | 0.814 ± 0.032 |
| 30% | 0.999 ± 0.012 | 0.995 ± 0.014 | 0.874 ± 0.015 |
| 40% | 1.104 ± 0.027 | 1.096 ± 0.028 | 0.938 ± 0.028 |
| 50% | 1.646 ± 0.067 | 1.646 ± 0.067 | 1.023 ± 0.023 |
| 60% | 1.579 ± 0.014 | 1.579 ± 0.014 | 1.140 ± 0.013 |
| Rate | SVT_OGpaper | SVT_partial | SVT |
|---|---|---|---|
| 10% | 0.0028 ± 0.0006 | 0.0028 ± 0.0007 | 0.0019 ± 0.0007 |
| 20% | 0.0095 ± 0.0026 | 0.0096 ± 0.0027 | 0.0059 ± 0.0011 |
| 30% | 0.0216 ± 0.0029 | 0.0215 ± 0.0030 | 0.0119 ± 0.0014 |
| 40% | 0.0393 ± 0.0045 | 0.0385 ± 0.0042 | 0.0226 ± 0.0048 |
| 50% | 0.1020 ± 0.0178 | 0.1020 ± 0.0178 | 0.0488 ± 0.0123 |
| 60% | 0.1600 ± 0.0309 | 0.1600 ± 0.0309 | 0.0952 ± 0.0312 |
| Rate | SVT_OGpaper | SVT_partial | SVT |
|---|---|---|---|
| 10% | 0.982 ± 0.004 | 0.982 ± 0.004 | 0.995 ± 0.003 |
| 20% | 0.956 ± 0.012 | 0.956 ± 0.012 | 0.991 ± 0.002 |
| 30% | 0.916 ± 0.020 | 0.915 ± 0.020 | 0.980 ± 0.003 |
| 40% | 0.858 ± 0.015 | 0.858 ± 0.015 | 0.960 ± 0.006 |
| 50% | 0.770 ± 0.025 | 0.770 ± 0.025 | 0.938 ± 0.009 |
| 60% | 0.695 ± 0.039 | 0.695 ± 0.039 | 0.889 ± 0.018 |
| Rate | SVT_OGpaper | SVT_partial | SVT |
|---|---|---|---|
| 10% | 0.0045 ± 0.0004 | 0.0045 ± 0.0005 | 0.0041 ± 0.0005 |
| 20% | 0.0124 ± 0.0004 | 0.0125 ± 0.0005 | 0.0094 ± 0.0012 |
| 30% | 0.0272 ± 0.0017 | 0.0271 ± 0.0015 | 0.0203 ± 0.0018 |
| 40% | 0.0562 ± 0.0051 | 0.0560 ± 0.0049 | 0.0403 ± 0.0022 |
| 50% | 0.1630 ± 0.0243 | 0.1630 ± 0.0243 | 0.0740 ± 0.0089 |
| 60% | 0.2020 ± 0.0048 | 0.2020 ± 0.0048 | 0.1272 ± 0.0116 |
| Method | Mean runtime (s) | Std runtime (s) | Min runtime (s) | Max runtime (s) |
|---|---|---|---|---|
| SVT_OGpaper | 5.465 ± 1.817 | 1.817 | 3.933 | 14.748 |
| SVT_partial | 15.810 ± 5.069 | 5.069 | 8.206 | 29.109 |
| SVT | 5.292 ± 1.267 | 1.267 | 3.896 | 9.304 |
| Rate | Zero · fixed | Zero · adaptive | Warm · fixed | Warm · adaptive |
|---|---|---|---|---|
| 10% | 0.811 ± 0.027 | 0.811 ± 0.027 | 0.734 ± 0.042 | 0.736 ± 0.043 |
| 20% | 0.922 ± 0.024 | 0.923 ± 0.024 | 0.809 ± 0.026 | 0.810 ± 0.025 |
| 30% | 1.008 ± 0.020 | 1.005 ± 0.020 | 0.877 ± 0.017 | 0.874 ± 0.016 |
| 40% | 1.113 ± 0.025 | 1.104 ± 0.023 | 0.949 ± 0.027 | 0.938 ± 0.026 |
| 50% | 1.656 ± 0.058 | 1.237 ± 0.011 | 1.052 ± 0.020 | 1.027 ± 0.017 |
| 60% | 1.620 ± 0.077 | 1.392 ± 0.024 | 1.168 ± 0.025 | 1.127 ± 0.022 |
| Rate | Zero · fixed | Zero · adaptive | Warm · fixed | Warm · adaptive |
|---|---|---|---|---|
| 10% | 0.003 ± 0.001 | 0.003 ± 0.001 | 0.002 ± 0.001 | 0.002 ± 0.001 |
| 20% | 0.010 ± 0.002 | 0.010 ± 0.002 | 0.006 ± 0.001 | 0.006 ± 0.001 |
| 30% | 0.021 ± 0.004 | 0.021 ± 0.004 | 0.012 ± 0.003 | 0.012 ± 0.003 |
| 40% | 0.037 ± 0.005 | 0.037 ± 0.004 | 0.022 ± 0.003 | 0.022 ± 0.004 |
| 50% | 0.105 ± 0.017 | 0.081 ± 0.018 | 0.049 ± 0.010 | 0.048 ± 0.010 |
| 60% | 0.175 ± 0.027 | 0.167 ± 0.021 | 0.095 ± 0.023 | 0.095 ± 0.025 |
| Rate | Zero · fixed | Zero · adaptive | Warm · fixed | Warm · adaptive |
|---|---|---|---|---|
| 10% | 0.005 ± 0.000 | 0.005 ± 0.000 | 0.004 ± 0.000 | 0.004 ± 0.000 |
| 20% | 0.012 ± 0.001 | 0.012 ± 0.001 | 0.009 ± 0.001 | 0.009 ± 0.001 |
| 30% | 0.027 ± 0.003 | 0.027 ± 0.003 | 0.021 ± 0.003 | 0.021 ± 0.003 |
| 40% | 0.052 ± 0.006 | 0.052 ± 0.006 | 0.040 ± 0.002 | 0.040 ± 0.002 |
| 50% | 0.163 ± 0.022 | 0.105 ± 0.009 | 0.072 ± 0.009 | 0.071 ± 0.008 |
| 60% | 0.220 ± 0.023 | 0.194 ± 0.018 | 0.126 ± 0.017 | 0.125 ± 0.015 |
| Rate | Zero · fixed | Zero · adaptive | Warm · fixed | Warm · adaptive |
|---|---|---|---|---|
| 10% | 0.982 ± 0.006 | 0.982 ± 0.006 | 0.996 ± 0.002 | 0.996 ± 0.002 |
| 20% | 0.951 ± 0.016 | 0.951 ± 0.016 | 0.990 ± 0.003 | 0.990 ± 0.002 |
| 30% | 0.906 ± 0.020 | 0.906 ± 0.020 | 0.979 ± 0.006 | 0.978 ± 0.005 |
| 40% | 0.856 ± 0.020 | 0.857 ± 0.021 | 0.962 ± 0.006 | 0.962 ± 0.006 |
| 50% | 0.765 ± 0.024 | 0.781 ± 0.025 | 0.936 ± 0.010 | 0.935 ± 0.009 |
| 60% | 0.682 ± 0.034 | 0.693 ± 0.047 | 0.894 ± 0.012 | 0.893 ± 0.014 |
| Method | Mean ± Std. | Min. | Max. |
|---|---|---|---|
| Zero init + fixed step size | 6.440 ± 1.616 | 4.385 | 12.716 |
| Zero init + adaptive step size | 6.147 ± 1.152 | 4.377 | 11.303 |
| Warm start + fixed step size | 6.378 ± 1.355 | 4.421 | 11.093 |
| Warm start + adaptive step size | 6.459 ± 1.669 | 4.229 | 12.460 |
| Missing | Pre. (ms) | SVD (ms) | SVT (s) | RF (s) | NF (s) | MF (s) | SI (s) |
|---|---|---|---|---|---|---|---|
| 20% | 0.18 ms | 4.09 ms | 8.97 s | 10.31 s | 19.28 s | 105.50 s | 0.48 s |
| 50% | 0.22 ms | 10.18 ms | 10.28 s | 7.75 s | 18.03 s | 76.64 s | 0.80 s |
| 80% | 0.19 ms | 12.81 ms | 8.22 s | 5.08 s | 13.30 s | 50.84 s | 0.46 s |
| Matrix size | Entries | SVT stage | RF stage | Total | RF share |
|---|---|---|---|---|---|
| 130,000 | 19.58 s | 46.44 s | 66.02 s | 70.3% | |
| 260,000 | 25.84 s | 104.19 s | 130.02 s | 80.1% | |
| 500,000 | 169.73 s | 653.89 s | 823.62 s | 79.4% | |
| 1,000,000 | 106.67 s | 727.89 s | 834.56 s | 87.2% | |
| 2,000,000 | 893.83 s | 5991.90 s | 6885.72 s | 87.0% |
| Rate | MissForest | kNN | Mean | Median | Half-min | SVT_OGpaper |
|---|---|---|---|---|---|---|
| 10% | 0.618 ± 0.020 | 0.888 ± 0.012 | 1.006 ± 0.001 | 1.029 ± 0.002 | 2.389 ± 0.063 | 0.811 ± 0.027 |
| 20% | 0.646 ± 0.021 | 0.901 ± 0.009 | 1.005 ± 0.001 | 1.029 ± 0.002 | 2.353 ± 0.043 | 0.922 ± 0.024 |
| 30% | 0.680 ± 0.007 | 0.922 ± 0.005 | 1.006 ± 0.001 | 1.031 ± 0.003 | 2.340 ± 0.049 | 1.008 ± 0.020 |
| 40% | 0.719 ± 0.009 | 0.949 ± 0.005 | 1.007 ± 0.001 | 1.030 ± 0.002 | 2.323 ± 0.027 | 1.113 ± 0.025 |
| 50% | 0.760 ± 0.008 | 0.970 ± 0.008 | 1.007 ± 0.001 | 1.031 ± 0.003 | 2.311 ± 0.030 | 1.656 ± 0.058 |
| 60% | 0.812 ± 0.009 | 1.001 ± 0.010 | 1.008 ± 0.001 | 1.031 ± 0.002 | 2.314 ± 0.016 | 1.620 ± 0.077 |
| Rate | MissForest | kNN | Mean | Median | Half-min | SVT_OGpaper |
|---|---|---|---|---|---|---|
| 10% | 0.002 ± 0.001 | 0.005 ± 0.001 | 0.007 ± 0.001 | 0.007 ± 0.001 | 0.038 ± 0.007 | 0.003 ± 0.001 |
| 20% | 0.005 ± 0.001 | 0.016 ± 0.003 | 0.018 ± 0.003 | 0.018 ± 0.004 | 0.081 ± 0.013 | 0.010 ± 0.002 |
| 30% | 0.010 ± 0.002 | 0.037 ± 0.007 | 0.037 ± 0.006 | 0.039 ± 0.007 | 0.153 ± 0.043 | 0.021 ± 0.004 |
| 40% | 0.020 ± 0.003 | 0.062 ± 0.020 | 0.062 ± 0.016 | 0.064 ± 0.015 | 0.243 ± 0.041 | 0.037 ± 0.005 |
| 50% | 0.030 ± 0.004 | 0.101 ± 0.028 | 0.099 ± 0.035 | 0.101 ± 0.034 | 0.400 ± 0.077 | 0.105 ± 0.017 |
| 60% | 0.053 ± 0.010 | 0.164 ± 0.049 | 0.215 ± 0.066 | 0.221 ± 0.073 | 0.625 ± 0.072 | 0.175 ± 0.027 |
| Rate | MissForest | kNN | Mean | Median | Half-min | SVT_OGpaper |
|---|---|---|---|---|---|---|
| 10% | 0.003 ± 0.001 | 0.009 ± 0.001 | 0.009 ± 0.001 | 0.010 ± 0.001 | 0.057 ± 0.008 | 0.005 ± 0.000 |
| 20% | 0.008 ± 0.001 | 0.021 ± 0.001 | 0.021 ± 0.001 | 0.022 ± 0.001 | 0.116 ± 0.015 | 0.012 ± 0.001 |
| 30% | 0.016 ± 0.002 | 0.043 ± 0.005 | 0.040 ± 0.006 | 0.042 ± 0.006 | 0.193 ± 0.034 | 0.027 ± 0.003 |
| 40% | 0.027 ± 0.003 | 0.069 ± 0.009 | 0.065 ± 0.009 | 0.067 ± 0.009 | 0.274 ± 0.023 | 0.052 ± 0.006 |
| 50% | 0.043 ± 0.002 | 0.101 ± 0.009 | 0.097 ± 0.011 | 0.098 ± 0.012 | 0.371 ± 0.037 | 0.163 ± 0.022 |
| 60% | 0.073 ± 0.010 | 0.166 ± 0.023 | 0.177 ± 0.014 | 0.181 ± 0.017 | 0.537 ± 0.036 | 0.220 ± 0.023 |
| Rate | MissForest | kNN | Mean | Median | Half-min | SVT_OGpaper |
|---|---|---|---|---|---|---|
| 10% | 0.995 ± 0.003 | 0.993 ± 0.003 | 0.992 ± 0.003 | 0.991 ± 0.003 | 0.892 ± 0.018 | 0.982 ± 0.006 |
| 20% | 0.991 ± 0.002 | 0.985 ± 0.002 | 0.983 ± 0.003 | 0.981 ± 0.004 | 0.814 ± 0.037 | 0.951 ± 0.016 |
| 30% | 0.984 ± 0.004 | 0.975 ± 0.004 | 0.970 ± 0.006 | 0.966 ± 0.007 | 0.719 ± 0.047 | 0.906 ± 0.020 |
| 40% | 0.972 ± 0.007 | 0.958 ± 0.010 | 0.957 ± 0.008 | 0.952 ± 0.008 | 0.666 ± 0.080 | 0.856 ± 0.020 |
| 50% | 0.956 ± 0.008 | 0.942 ± 0.008 | 0.940 ± 0.010 | 0.931 ± 0.012 | 0.582 ± 0.072 | 0.765 ± 0.024 |
| 60% | 0.938 ± 0.010 | 0.916 ± 0.013 | 0.909 ± 0.017 | 0.900 ± 0.022 | 0.511 ± 0.088 | 0.682 ± 0.034 |
| Rate | MissForest | kNN | Mean | Median | Half-min | SVT_OGpaper |
|---|---|---|---|---|---|---|
| 10% | 81.200 ± 7.815 | 111.400 ± 7.575 | 126.900 ± 5.990 | 97.600 ± 7.619 | 83.900 ± 12.991 | 49.900 ± 9.231 |
| 20% | 158.300 ± 9.031 | 216.800 ± 7.495 | 253.000 ± 13.241 | 182.700 ± 14.244 | 153.300 ± 18.252 | 119.900 ± 14.873 |
| 30% | 245.000 ± 19.067 | 328.300 ± 12.650 | 380.200 ± 11.013 | 271.700 ± 16.207 | 222.600 ± 27.240 | 162.300 ± 15.805 |
| 40% | 327.600 ± 17.494 | 434.600 ± 12.011 | 509.400 ± 13.032 | 369.000 ± 22.784 | 279.400 ± 19.873 | 215.300 ± 16.687 |
| 50% | 395.600 ± 27.293 | 538.500 ± 13.938 | 624.700 ± 10.605 | 441.900 ± 19.451 | 356.300 ± 29.208 | 306.200 ± 17.415 |
| 60% | 480.600 ± 23.114 | 654.100 ± 23.473 | 740.500 ± 16.939 | 518.100 ± 16.462 | 398.600 ± 23.177 | 345.900 ± 28.136 |
| Rate | MissForest | kNN | Mean | Median | Half-min | SVT_OGpaper |
|---|---|---|---|---|---|---|
| 10% | 0.001 ± 0.001 | 0.004 ± 0.001 | 0.005 ± 0.002 | 0.004 ± 0.002 | 0.001 ± 0.000 | 0.001 ± 0.001 |
| 20% | 0.003 ± 0.001 | 0.010 ± 0.005 | 0.012 ± 0.005 | 0.009 ± 0.005 | 0.002 ± 0.001 | 0.003 ± 0.002 |
| 30% | 0.011 ± 0.003 | 0.038 ± 0.018 | 0.037 ± 0.017 | 0.029 ± 0.012 | 0.004 ± 0.001 | 0.005 ± 0.002 |
| 40% | 0.021 ± 0.011 | 0.056 ± 0.021 | 0.066 ± 0.035 | 0.047 ± 0.020 | 0.006 ± 0.001 | 0.006 ± 0.002 |
| 50% | 0.032 ± 0.008 | 0.108 ± 0.036 | 0.122 ± 0.033 | 0.088 ± 0.023 | 0.009 ± 0.001 | 0.010 ± 0.003 |
| 60% | 0.050 ± 0.013 | 0.170 ± 0.061 | 0.191 ± 0.040 | 0.129 ± 0.027 | 0.010 ± 0.002 | 0.014 ± 0.003 |
| Rate | MissForest | kNN | Mean | Median | Half-min | SVT_OGpaper |
|---|---|---|---|---|---|---|
| 10% | 0.002 ± 0.001 | 0.005 ± 0.003 | 0.006 ± 0.003 | 0.005 ± 0.002 | 0.001 ± 0.000 | 0.002 ± 0.001 |
| 20% | 0.006 ± 0.002 | 0.013 ± 0.004 | 0.013 ± 0.004 | 0.009 ± 0.003 | 0.002 ± 0.000 | 0.004 ± 0.001 |
| 30% | 0.015 ± 0.005 | 0.041 ± 0.021 | 0.040 ± 0.016 | 0.031 ± 0.012 | 0.005 ± 0.001 | 0.009 ± 0.004 |
| 40% | 0.024 ± 0.008 | 0.061 ± 0.029 | 0.067 ± 0.025 | 0.051 ± 0.019 | 0.007 ± 0.002 | 0.009 ± 0.002 |
| 50% | 0.039 ± 0.006 | 0.090 ± 0.028 | 0.107 ± 0.020 | 0.080 ± 0.015 | 0.010 ± 0.001 | 0.018 ± 0.003 |
| 60% | 0.057 ± 0.015 | 0.128 ± 0.052 | 0.155 ± 0.040 | 0.114 ± 0.029 | 0.012 ± 0.003 | 0.024 ± 0.005 |
| Rate | MissForest | kNN | Mean | Median | Half-min | SVT_OGpaper |
|---|---|---|---|---|---|---|
| 10% | 0.991 ± 0.007 | 0.962 ± 0.024 | 0.942 ± 0.036 | 0.956 ± 0.027 | 0.995 ± 0.003 | 0.997 ± 0.001 |
| 20% | 0.983 ± 0.006 | 0.948 ± 0.022 | 0.924 ± 0.030 | 0.946 ± 0.024 | 0.994 ± 0.002 | 0.992 ± 0.004 |
| 30% | 0.964 ± 0.015 | 0.890 ± 0.048 | 0.849 ± 0.042 | 0.888 ± 0.032 | 0.989 ± 0.004 | 0.988 ± 0.006 |
| 40% | 0.950 ± 0.012 | 0.842 ± 0.046 | 0.787 ± 0.037 | 0.838 ± 0.031 | 0.987 ± 0.003 | 0.986 ± 0.003 |
| 50% | 0.948 ± 0.013 | 0.847 ± 0.035 | 0.788 ± 0.028 | 0.842 ± 0.023 | 0.986 ± 0.004 | 0.975 ± 0.006 |
| 60% | 0.921 ± 0.016 | 0.798 ± 0.079 | 0.734 ± 0.061 | 0.803 ± 0.042 | 0.982 ± 0.005 | 0.967 ± 0.009 |
| Rate | MissForest | kNN | Mean | Median | Half-min | SVT_OGpaper |
|---|---|---|---|---|---|---|
| 10% | 0.842 ± 0.032 | 1.049 ± 0.029 | 1.003 ± 0.001 | 1.147 ± 0.012 | 1.829 ± 0.057 | 1.451 ± 0.037 |
| 20% | 0.892 ± 0.020 | 1.072 ± 0.026 | 1.003 ± 0.001 | 1.141 ± 0.011 | 1.814 ± 0.032 | 1.460 ± 0.038 |
| 30% | 0.950 ± 0.015 | 1.060 ± 0.024 | 1.002 ± 0.001 | 1.134 ± 0.005 | 1.818 ± 0.020 | 1.520 ± 0.021 |
| 40% | 1.020 ± 0.023 | 1.011 ± 0.010 | 1.003 ± 0.001 | 1.138 ± 0.005 | 1.807 ± 0.056 | 1.401 ± 0.016 |
| 50% | 1.072 ± 0.021 | 1.006 ± 0.006 | 1.002 ± 0.001 | 1.137 ± 0.006 | 1.799 ± 0.039 | 1.689 ± 0.036 |
| 60% | 1.124 ± 0.031 | 1.018 ± 0.005 | 1.003 ± 0.001 | 1.140 ± 0.004 | 1.759 ± 0.047 | 1.610 ± 0.031 |
| Rate | MissForest | kNN | Mean | Median | Half-min | SVT_OGpaper |
|---|---|---|---|---|---|---|
| 10% | 0.145 ± 0.017 | 0.271 ± 0.027 | 0.253 ± 0.014 | 0.253 ± 0.014 | 0.338 ± 0.021 | 0.271 ± 0.016 |
| 20% | 0.173 ± 0.007 | 0.254 ± 0.004 | 0.253 ± 0.012 | 0.253 ± 0.012 | 0.333 ± 0.010 | 0.274 ± 0.010 |
| 30% | 0.194 ± 0.010 | 0.243 ± 0.018 | 0.247 ± 0.007 | 0.247 ± 0.007 | 0.331 ± 0.010 | 0.281 ± 0.006 |
| 40% | 0.227 ± 0.010 | 0.236 ± 0.008 | 0.247 ± 0.006 | 0.247 ± 0.006 | 0.332 ± 0.009 | 0.245 ± 0.018 |
| 50% | 0.252 ± 0.014 | 0.243 ± 0.005 | 0.250 ± 0.006 | 0.250 ± 0.006 | 0.333 ± 0.009 | 0.295 ± 0.013 |
| 60% | 0.270 ± 0.017 | 0.254 ± 0.005 | 0.253 ± 0.004 | 0.253 ± 0.004 | 0.333 ± 0.006 | 0.289 ± 0.007 |
| Rate | MissForest | kNN | Mean | Median | Half-min | SVT_OGpaper |
|---|---|---|---|---|---|---|
| 10% | 0.130 ± 0.008 | 0.228 ± 0.019 | 0.215 ± 0.009 | 0.212 ± 0.009 | 0.342 ± 0.018 | 0.265 ± 0.009 |
| 20% | 0.147 ± 0.005 | 0.216 ± 0.008 | 0.215 ± 0.008 | 0.209 ± 0.009 | 0.337 ± 0.008 | 0.269 ± 0.012 |
| 30% | 0.161 ± 0.006 | 0.208 ± 0.012 | 0.212 ± 0.006 | 0.205 ± 0.006 | 0.333 ± 0.008 | 0.280 ± 0.007 |
| 40% | 0.185 ± 0.007 | 0.202 ± 0.005 | 0.211 ± 0.004 | 0.205 ± 0.005 | 0.335 ± 0.008 | 0.246 ± 0.012 |
| 50% | 0.204 ± 0.008 | 0.207 ± 0.003 | 0.214 ± 0.005 | 0.208 ± 0.005 | 0.335 ± 0.008 | 0.304 ± 0.012 |
| 60% | 0.221 ± 0.010 | 0.216 ± 0.004 | 0.217 ± 0.003 | 0.211 ± 0.003 | 0.333 ± 0.006 | 0.297 ± 0.006 |
| Rate | MissForest | kNN | Mean | Median | Half-min | SVT_OGpaper |
|---|---|---|---|---|---|---|
| 10% | 0.023 ± 0.008 | 0.038 ± 0.017 | 0.100 ± 0.021 | 0.113 ± 0.020 | 0.330 ± 0.044 | 0.339 ± 0.045 |
| 20% | 0.034 ± 0.012 | 0.114 ± 0.029 | 0.192 ± 0.026 | 0.217 ± 0.030 | 0.479 ± 0.026 | 0.449 ± 0.034 |
| 30% | 0.050 ± 0.023 | 0.173 ± 0.033 | 0.276 ± 0.033 | 0.324 ± 0.041 | 0.571 ± 0.056 | 0.499 ± 0.056 |
| 40% | 0.065 ± 0.017 | 0.217 ± 0.020 | 0.356 ± 0.040 | 0.414 ± 0.052 | 0.630 ± 0.041 | 0.760 ± 0.096 |
| 50% | 0.059 ± 0.032 | 0.325 ± 0.048 | 0.448 ± 0.035 | 0.559 ± 0.038 | 0.672 ± 0.034 | 1.180 ± 0.118 |
| 60% | 0.060 ± 0.034 | 0.480 ± 0.054 | 0.562 ± 0.066 | 0.697 ± 0.109 | 0.689 ± 0.055 | 1.152 ± 0.096 |
| Rate | MissForest | kNN | Mean | Median | Half-min | SVT_OGpaper |
|---|---|---|---|---|---|---|
| 10% | 0.024 ± 0.004 | 0.045 ± 0.006 | 0.043 ± 0.003 | 0.060 ± 0.005 | 0.122 ± 0.007 | 0.112 ± 0.011 |
| 20% | 0.067 ± 0.008 | 0.096 ± 0.007 | 0.091 ± 0.006 | 0.121 ± 0.008 | 0.227 ± 0.020 | 0.225 ± 0.020 |
| 30% | 0.124 ± 0.010 | 0.154 ± 0.020 | 0.142 ± 0.005 | 0.185 ± 0.009 | 0.335 ± 0.015 | 0.361 ± 0.022 |
| 40% | 0.208 ± 0.011 | 0.198 ± 0.011 | 0.198 ± 0.007 | 0.257 ± 0.008 | 0.436 ± 0.017 | 0.367 ± 0.019 |
| 50% | 0.319 ± 0.021 | 0.284 ± 0.011 | 0.282 ± 0.014 | 0.354 ± 0.016 | 0.559 ± 0.018 | 0.492 ± 0.025 |
| 60% | 0.441 ± 0.015 | 0.393 ± 0.018 | 0.378 ± 0.011 | 0.455 ± 0.013 | 0.657 ± 0.024 | 0.584 ± 0.020 |
| Rate | MissForest | kNN | Mean | Median | Half-min | SVT_OGpaper |
|---|---|---|---|---|---|---|
| 10% | 0.027 ± 0.004 | 0.047 ± 0.007 | 0.046 ± 0.004 | 0.062 ± 0.006 | 0.140 ± 0.011 | 0.134 ± 0.014 |
| 20% | 0.069 ± 0.007 | 0.100 ± 0.006 | 0.096 ± 0.006 | 0.125 ± 0.009 | 0.254 ± 0.022 | 0.257 ± 0.024 |
| 30% | 0.124 ± 0.007 | 0.157 ± 0.017 | 0.148 ± 0.007 | 0.187 ± 0.010 | 0.364 ± 0.020 | 0.393 ± 0.026 |
| 40% | 0.207 ± 0.012 | 0.203 ± 0.013 | 0.206 ± 0.009 | 0.260 ± 0.010 | 0.466 ± 0.020 | 0.403 ± 0.020 |
| 50% | 0.314 ± 0.020 | 0.290 ± 0.012 | 0.292 ± 0.014 | 0.358 ± 0.019 | 0.589 ± 0.020 | 0.526 ± 0.024 |
| 60% | 0.439 ± 0.021 | 0.396 ± 0.020 | 0.385 ± 0.016 | 0.455 ± 0.016 | 0.680 ± 0.025 | 0.610 ± 0.023 |
| Rate | MissForest | kNN | Mean | Median | Half-min | SVT_OGpaper |
|---|---|---|---|---|---|---|
| 10% | 0.993 ± 0.004 | 0.993 ± 0.004 | 0.983 ± 0.008 | 0.995 ± 0.002 | 0.892 ± 0.031 | 0.761 ± 0.033 |
| 20% | 0.970 ± 0.008 | 0.971 ± 0.015 | 0.952 ± 0.016 | 0.984 ± 0.008 | 0.854 ± 0.035 | 0.631 ± 0.045 |
| 30% | 0.926 ± 0.015 | 0.966 ± 0.015 | 0.927 ± 0.024 | 0.973 ± 0.014 | 0.763 ± 0.040 | 0.486 ± 0.047 |
| 40% | 0.885 ± 0.032 | 0.959 ± 0.018 | 0.920 ± 0.023 | 0.970 ± 0.015 | 0.661 ± 0.056 | 0.294 ± 0.053 |
| 50% | 0.846 ± 0.023 | 0.927 ± 0.021 | 0.890 ± 0.029 | 0.951 ± 0.015 | 0.675 ± 0.082 | 0.522 ± 0.113 |
| 60% | 0.794 ± 0.032 | 0.919 ± 0.029 | 0.875 ± 0.036 | 0.941 ± 0.029 | 0.600 ± 0.064 | 0.325 ± 0.119 |
| Rate | MissForest | kNN | Mean | Median | Half-min | SVT_OGpaper |
|---|---|---|---|---|---|---|
| 10% | 0.852 ± 0.033 | 1.063 ± 0.061 | 1.009 ± 0.005 | 1.119 ± 0.012 | 1.808 ± 0.066 | 1.415 ± 0.046 |
| 20% | 0.920 ± 0.029 | 1.060 ± 0.021 | 1.012 ± 0.003 | 1.132 ± 0.005 | 1.779 ± 0.050 | 1.458 ± 0.044 |
| 30% | 0.979 ± 0.020 | 1.068 ± 0.019 | 1.018 ± 0.003 | 1.124 ± 0.008 | 1.774 ± 0.039 | 1.534 ± 0.029 |
| 40% | 1.032 ± 0.027 | 1.055 ± 0.010 | 1.023 ± 0.003 | 1.124 ± 0.007 | 1.733 ± 0.040 | 1.371 ± 0.030 |
| 50% | 1.084 ± 0.019 | 1.068 ± 0.004 | 1.033 ± 0.004 | 1.127 ± 0.002 | 1.702 ± 0.031 | 2.007 ± 0.065 |
| 60% | 1.107 ± 0.026 | 1.099 ± 0.008 | 1.046 ± 0.008 | 1.132 ± 0.006 | 1.687 ± 0.020 | 1.965 ± 0.056 |
| Rate | MissForest | kNN | Mean | Median | Half-min | SVT_OGpaper |
|---|---|---|---|---|---|---|
| 10% | 0.134 ± 0.019 | 0.256 ± 0.030 | 0.229 ± 0.022 | 0.229 ± 0.022 | 0.301 ± 0.018 | 0.243 ± 0.017 |
| 20% | 0.178 ± 0.012 | 0.244 ± 0.024 | 0.247 ± 0.013 | 0.247 ± 0.013 | 0.318 ± 0.010 | 0.276 ± 0.014 |
| 30% | 0.204 ± 0.016 | 0.236 ± 0.011 | 0.235 ± 0.013 | 0.235 ± 0.013 | 0.310 ± 0.016 | 0.270 ± 0.015 |
| 40% | 0.237 ± 0.016 | 0.242 ± 0.006 | 0.240 ± 0.006 | 0.240 ± 0.006 | 0.310 ± 0.007 | 0.241 ± 0.011 |
| 50% | 0.258 ± 0.012 | 0.247 ± 0.005 | 0.244 ± 0.002 | 0.244 ± 0.002 | 0.315 ± 0.003 | 0.291 ± 0.009 |
| 60% | 0.266 ± 0.010 | 0.259 ± 0.005 | 0.242 ± 0.005 | 0.242 ± 0.005 | 0.313 ± 0.006 | 0.293 ± 0.008 |
| Rate | MissForest | kNN | Mean | Median | Half-min | SVT_OGpaper |
|---|---|---|---|---|---|---|
| 10% | 0.128 ± 0.012 | 0.216 ± 0.019 | 0.203 ± 0.017 | 0.194 ± 0.018 | 0.323 ± 0.017 | 0.250 ± 0.012 |
| 20% | 0.151 ± 0.008 | 0.214 ± 0.012 | 0.220 ± 0.011 | 0.212 ± 0.010 | 0.341 ± 0.009 | 0.278 ± 0.012 |
| 30% | 0.168 ± 0.012 | 0.214 ± 0.010 | 0.211 ± 0.010 | 0.202 ± 0.010 | 0.334 ± 0.010 | 0.280 ± 0.012 |
| 40% | 0.188 ± 0.008 | 0.223 ± 0.003 | 0.219 ± 0.004 | 0.213 ± 0.004 | 0.337 ± 0.005 | 0.248 ± 0.009 |
| 50% | 0.205 ± 0.007 | 0.226 ± 0.005 | 0.222 ± 0.003 | 0.217 ± 0.004 | 0.338 ± 0.005 | 0.356 ± 0.005 |
| 60% | 0.215 ± 0.009 | 0.238 ± 0.003 | 0.225 ± 0.004 | 0.219 ± 0.004 | 0.338 ± 0.005 | 0.347 ± 0.009 |
| Rate | MissForest | kNN | Mean | Median | Half-min | SVT_OGpaper |
|---|---|---|---|---|---|---|
| 10% | 0.021 ± 0.008 | 0.026 ± 0.024 | 0.068 ± 0.022 | 0.057 ± 0.023 | 0.206 ± 0.040 | 0.215 ± 0.042 |
| 20% | 0.031 ± 0.015 | 0.066 ± 0.019 | 0.143 ± 0.019 | 0.129 ± 0.022 | 0.318 ± 0.050 | 0.320 ± 0.058 |
| 30% | 0.028 ± 0.019 | 0.144 ± 0.034 | 0.225 ± 0.030 | 0.210 ± 0.039 | 0.374 ± 0.055 | 0.353 ± 0.062 |
| 40% | 0.060 ± 0.010 | 0.228 ± 0.068 | 0.275 ± 0.051 | 0.264 ± 0.052 | 0.385 ± 0.047 | 0.513 ± 0.081 |
| 50% | 0.070 ± 0.031 | 0.323 ± 0.050 | 0.341 ± 0.036 | 0.354 ± 0.056 | 0.394 ± 0.044 | 1.264 ± 0.138 |
| 60% | 0.057 ± 0.027 | 0.453 ± 0.075 | 0.421 ± 0.045 | 0.469 ± 0.049 | 0.418 ± 0.059 | 1.339 ± 0.156 |
| Rate | MissForest | kNN | Mean | Median | Half-min | SVT_OGpaper |
|---|---|---|---|---|---|---|
| 10% | 0.022 ± 0.004 | 0.038 ± 0.004 | 0.038 ± 0.003 | 0.049 ± 0.005 | 0.091 ± 0.006 | 0.081 ± 0.005 |
| 20% | 0.070 ± 0.009 | 0.085 ± 0.006 | 0.086 ± 0.004 | 0.109 ± 0.004 | 0.179 ± 0.010 | 0.186 ± 0.010 |
| 30% | 0.131 ± 0.008 | 0.139 ± 0.005 | 0.141 ± 0.005 | 0.162 ± 0.006 | 0.243 ± 0.011 | 0.284 ± 0.018 |
| 40% | 0.213 ± 0.029 | 0.224 ± 0.015 | 0.214 ± 0.010 | 0.233 ± 0.011 | 0.310 ± 0.009 | 0.289 ± 0.012 |
| 50% | 0.312 ± 0.021 | 0.345 ± 0.012 | 0.307 ± 0.011 | 0.320 ± 0.010 | 0.365 ± 0.015 | 0.458 ± 0.021 |
| 60% | 0.404 ± 0.027 | 0.516 ± 0.021 | 0.421 ± 0.018 | 0.400 ± 0.010 | 0.410 ± 0.009 | 0.494 ± 0.024 |
| Rate | MissForest | kNN | Mean | Median | Half-min | SVT_OGpaper |
|---|---|---|---|---|---|---|
| 10% | 0.023 ± 0.004 | 0.039 ± 0.003 | 0.039 ± 0.003 | 0.049 ± 0.005 | 0.101 ± 0.006 | 0.095 ± 0.006 |
| 20% | 0.071 ± 0.009 | 0.089 ± 0.007 | 0.091 ± 0.004 | 0.113 ± 0.005 | 0.195 ± 0.011 | 0.208 ± 0.012 |
| 30% | 0.130 ± 0.008 | 0.145 ± 0.009 | 0.147 ± 0.007 | 0.166 ± 0.009 | 0.263 ± 0.018 | 0.307 ± 0.023 |
| 40% | 0.206 ± 0.025 | 0.235 ± 0.018 | 0.224 ± 0.013 | 0.239 ± 0.016 | 0.332 ± 0.012 | 0.318 ± 0.012 |
| 50% | 0.313 ± 0.021 | 0.358 ± 0.016 | 0.319 ± 0.013 | 0.327 ± 0.011 | 0.385 ± 0.017 | 0.465 ± 0.021 |
| 60% | 0.398 ± 0.033 | 0.537 ± 0.024 | 0.435 ± 0.021 | 0.409 ± 0.015 | 0.430 ± 0.010 | 0.499 ± 0.023 |
| Rate | MissForest | kNN | Mean | Median | Half-min | SVT_OGpaper |
|---|---|---|---|---|---|---|
| 10% | 0.994 ± 0.003 | 0.995 ± 0.004 | 0.968 ± 0.010 | 0.987 ± 0.006 | 0.886 ± 0.021 | 0.808 ± 0.026 |
| 20% | 0.972 ± 0.011 | 0.988 ± 0.005 | 0.918 ± 0.025 | 0.952 ± 0.017 | 0.808 ± 0.034 | 0.682 ± 0.047 |
| 30% | 0.940 ± 0.023 | 0.970 ± 0.015 | 0.889 ± 0.023 | 0.921 ± 0.022 | 0.698 ± 0.028 | 0.551 ± 0.042 |
| 40% | 0.879 ± 0.021 | 0.931 ± 0.020 | 0.832 ± 0.027 | 0.854 ± 0.029 | 0.638 ± 0.042 | 0.488 ± 0.045 |
| 50% | 0.810 ± 0.021 | 0.827 ± 0.037 | 0.783 ± 0.042 | 0.767 ± 0.041 | 0.533 ± 0.041 | 0.629 ± 0.045 |
| 60% | 0.751 ± 0.040 | 0.707 ± 0.063 | 0.739 ± 0.036 | 0.674 ± 0.046 | 0.473 ± 0.056 | 0.541 ± 0.064 |
| Method | Mean (s) | Std (s) | Min (s) | Max (s) |
|---|---|---|---|---|
| MissForest | 77.787 | 21.426 | 49.290 | 116.269 |
| kNN | 0.040 | 0.009 | 0.021 | 0.063 |
| Mean | 0.001 | 0.001 | 0.000 | 0.004 |
| Median | 0.002 | 0.001 | 0.001 | 0.006 |
| Half-min | 0.000 | 0.000 | 0.000 | 0.002 |
| SVT_OGpaper | 5.889 | 1.261 | 4.180 | 12.519 |
| Method | Mean (s) | Std (s) | Min (s) | Max (s) |
|---|---|---|---|---|
| MissForest | 89.438 | 22.845 | 11.206 | 111.187 |
| kNN | 0.025 | 0.012 | 0.006 | 0.060 |
| Mean | 0.001 | 0.001 | 0.000 | 0.005 |
| Median | 0.002 | 0.001 | 0.001 | 0.005 |
| Half-min | 0.000 | 0.000 | 0.000 | 0.001 |
| SVT_OGpaper | 5.611 | 0.769 | 4.911 | 9.745 |
| Method | Mean (s) | Std (s) | Min (s) | Max (s) |
|---|---|---|---|---|
| MissForest | 5.934 | 0.187 | 5.516 | 6.454 |
| kNN | 0.034 | 0.024 | 0.010 | 0.144 |
| Mean | 0.001 | 0.000 | 0.000 | 0.004 |
| Median | 0.001 | 0.000 | 0.001 | 0.003 |
| Half-min | 0.000 | 0.000 | 0.000 | 0.003 |
| SVT_OGpaper | 0.119 | 0.096 | 0.002 | 0.285 |
| Method | Mean (s) | Std (s) | Min (s) | Max (s) |
|---|---|---|---|---|
| MissForest | 5.973 | 0.198 | 5.719 | 6.754 |
| kNN | 0.030 | 0.018 | 0.008 | 0.140 |
| Mean | 0.001 | 0.001 | 0.000 | 0.007 |
| Median | 0.001 | 0.000 | 0.001 | 0.003 |
| Half-min | 0.000 | 0.000 | 0.000 | 0.001 |
| SVT_OGpaper | 0.120 | 0.096 | 0.002 | 0.305 |