各个数据集的特征以及样本数量:
数据集 | 特征数量 | 样本数量 |
---|---|---|
裴丹 | 42 | 10万+ |
apascal | 64 | 1.2万+ |
bank | 62 | 4.1万+ |
lung | 3311 | 140+ |
probe | 34 | 6.4万+ |
secom | 590 | 1500+ |
U2r | 33 | 6万+ |
conducted with 200 epochs and 10 individual runs., just use train.py to train model and use test.py to test
model
Criterion / Improvements | Ori | Neither | Pairwise Loss | Momentum Updating | Both |
---|---|---|---|---|---|
Sample Distance | / | AUROC: 0.8445 AUPR: 0.0574 |
AUROC: 0.8425 AUPR: 0.0573 |
AUC-ROC: 0.8237 AUC-PR: 0.0472 |
AUC-ROC: 0.8321 AUC-PR: 0.0519 |
LOF | / | AUROC: 0.5193 AUPR: 0.0133 |
AUROC: 0.5039 AUPR: 0.0130 |
AUC-ROC: 0.5384 AUC-PR: 0.0138 |
AUC-ROC: 0.5281 AUC-PR: 0.0136 |
Isolation Forest | / | AUROC: 0.6341 AUPR: 0.0254 |
AUROC: 0.6745 AUPR: 0.0343 |
AUC-ROC: 0.7919 AUC-PR: 0.0425 |
AUC-ROC: 0.8011 AUC-PR: 0.0470 |
Criterion / Improvements | Ori | Neither | Pairwise Loss | Momentum Updating | Both |
---|---|---|---|---|---|
Sample Distance | / | AUC-ROC: 0.7377 AUC-PR: 0.3414 |
/ | AUC-ROC: 0.7441 AUC-PR: 0.3190 |
AUC-ROC: 0.7435 AUC-PR: 0.3336 |
LOF | / | / | / | / | / |
Isolation Forest | / | AUC-ROC: 0.6892 AUC-PR: 0.2975 |
/ | AUC-ROC: 0.7268 AUC-PR: 0.3172 |
AUC-ROC: 0.7301 AUC-PR: 0.3295 |
Criterion / Improvements | Ori | Neither | Pairwise Loss | Momentum Updating | Both |
---|---|---|---|---|---|
Sample Distance | / | AUC-ROC: 0.9260 AUC-PR: 0.5999 |
/ | AUC-ROC: 0.9880 AUC-PR: 0.7782 |
AUC-ROC: 0.9955 AUC-PR: 0.9265 |
LOF | / | / | / | / | / |
Isolation Forest | / | AUC-ROC: 0.9916 AUC-PR: 0.8617 |
/ | AUC-ROC: 0.9740 AUC-PR: 0.6881 |
AUC-ROC: 0.9818 AUC-PR: 0.7351 |