Research Article
Deep Learning-Based Prediction of Physical Stability considering Class Imbalance for Amorphous Solid Dispersions
Table 1
The performance comparison of models applying various sampling techniques to the proposed neural net architecture.
| | Without sampling | Oversampling | Undersampling | Hybrid sampling | SMOTE | ADASYN | TL | ENN | OSS | CNN | Smote+ENN | Smote+TL |
| Accuracy | Training set | 0.864 | 0.998 | 0.99 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 5-fold cross-validation | | | | | | | | | | Test set | 0.85 | 0.9 | 0.8 | 0.9 | 0.8 | 0.8 | 0.65 | 0.95 | 0.9 | Precision | Training set | 0.865 | 0.998 | 0.99 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 5-fold cross-validation | | | | | | | | | | Test set | 0.864 | 0.86 | 0.864 | 0.86 | 0.887 | 0.826 | 0.725 | 0.96 | 0.86 | Recall | Training set | 0.864 | 0.998 | 0.99 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 5-fold cross-validation | | | | | | | | | | Test set | 0.85 | 0.9 | 0.8 | 0.9 | 0.8 | 0.8 | 0.65 | 0.95 | 0.9 | -score | Training set | 0.86 | 0.998 | 0.99 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 0.998 | 5-fold cross-validation | | | | | | | | | | Test set | 0.842 | 0.878 | 0.812 | 0.878 | 0.825 | 0.803 | 0.676 | 0.952 | 0.878 |
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