Table 4. Performance of the proposed architectures in terms of logarithmic loss.
| Subset | Baseline | Semi | Sym | Zero | SVM | k-NN | DecTree |
|---|---|---|---|---|---|---|---|
| 0.3004 | 0.2708 | 0.2657 | 0.2716 | 0.2421 | 4.3670 | 4.1889 | |
| C | 0.2829 | 0.2757 | 0.2868 | 0.2609 | 0.2614 | 2.6884 | 3.5001 |
| H | 0.2169 | 0.2274 | 0.2422 | 0.2031 | 0.1984 | 0.7178 | 3.2175 |
| S | 0.1710 | 0.1475 | 0.1489 | 0.1359 | 0.1273 | 0.9366 | 1.6893 |
| CH | 0.2210 | 0.2054 | 0.2286 | 0.2123 | 0.2196 | 1.0477 | 2.8509 |
| CS | 0.1594 | 0.1469 | 0.1240 | 0.1464 | 0.1248 | 0.4036 | 1.7687 |
| HS | 0.1632 | 0.1786 | 0.1615 | 0.1622 | 0.1225 | 0.3238 | 1.8098 |
| CHS | 0.1563 | 0.1577 | 0.1494 | 0.1514 | 0.1099 | 0.4037 | 1.8906 |
| Best | 0 | 1 | 1 | 1 | 4 | 0 | 0 |
Notes:
The subset of observable screening strategies include: Cytology (C), Hinselmann (H), and Schiller (S). Area under the Precision–Recall curve.
Baseline, deep feed-forward neural network; Semi, semi-supervised dimensionality reduction (Eq. 2); Sym, symmetry mapping dimensionality reduction (Eq. 4); Zero, zero mapping dimensionality reduction (Eq. 5); SVM, support vector machine; k-NN, k-nearest neighbors; DecTree, decision tree.
We highlight the best performing models in bold.