Research map / Probabilistic and scientific modeling / Molecules and proteins
Concurrent Multi-Label Prediction in Event Streams
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use TCMBN instead of the strongest listed baseline | concurrent multi-label prediction · PM | weighted ROC-AUC: 0.752(0.009) -> 0.764(0.011) | Helped |
| Use TCMBN instead of the strongest listed baseline | concurrent multi-label prediction · HM | weighted ROC-AUC: 0.755(0.005) -> 0.765(0.005) | Helped |
| Use TCMBN instead of the strongest listed baseline | concurrent multi-label prediction · MIMIC III | F1: 0.288 -> 0.354 | Helped |
| Use TCMBN instead of the strongest listed baseline | concurrent multi-label prediction · Dunnhumby | ROC-AUC: 0.697 -> 0.691 | Hurt |
| Use TCMBN instead of the strongest listed baseline | concurrent multi-label prediction · Defi | F1: 0.462 -> 0.469 | Helped |
| Remove background knowledge loss (ablation) | TCMBN · PM | weighted F1: 0.684(0.003) vs. 0.686(0.004) | Hurt |
| Remove background knowledge loss (ablation) | TCMBN · HM | weighted F1: 0.682(0.003) vs. 0.683(0.002) | Hurt |
| Remove background knowledge loss (ablation) | TCMBN · Defi | F1: 0.464 vs. 0.469 | Hurt |
| Vary the number of Bernoulli mixture components (ablation) | TCMBN · PM and HM | weighted ROC-AUC | No clear effect |
| Use 32 instead of 64 mixture components (ablation) | TCMBN · PM and HM | predictive performance | Mixed |
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