Table 4 Accuracy of STS in real data applications
From: Missing value imputation in high-dimensional phenomic data: imputable or not, and how?
Data | m% | Continuous variables | Nominal variables | Ordinal variables | |||
|---|---|---|---|---|---|---|---|
Predicted optimal method (No. of time selected) | Accuracy | Predicted optimal method (No. of time selected) | Accuracy | Predicted optimal method (No. of time selected) | Accuracy | ||
COPD | 5% | KNN-V(10), RF(10) | 100% | RF(10), KNN-A(8), KNN-V(2) | 100% | RF(20) | 100% |
20% | KNN-V(13), RF(6), KNN-H(1) | 100% | RF(14), KNN-A(4), KNN-V(2) | 100% | RF(20) | 100% | |
40% | KNN-V(10), RF(10) | 100% | KNN-V(16), RF(1), KNN-A(3) | 95% | RF(20) | 100% | |
LTRC | 5% | KNN-V(15), KNN-A(3), RF(2) | 95% | RF(14), KNN-A(3), KNN-V(3) | 75% | RF(19), KNN-A(1) | 100% |
20% | KNN-V(12), RF(8) | 85% | RF(15), KNN-V(1), KNN-A(4) | 100% | RF(16), KNN-A(4) | 100% | |
40% | RF(13), KNN-V(7) | 90% | KNN-A(13), RF(6), KNN-V(1) | 100% | RF(20) | 100% | |
SARP | 5% | KNN-V(13), KNN-A(6), RF(1) | 100% | KNN-A(20) | 100% | RF(18), KNN-H(2) | 100% |
20% | KNN-V(16), KNN-A(4) | 100% | KNN-A(20) | 100% | RF(16), KNN-H(4) | 100% | |
40% | KNN-V(17), KNN-A(3) | 100% | KNN-A(20) | 100% | RF(20) | 100% | |