Outlier Detection on Yeast (AUC-PR)
0.5504AUC-PRLUNAR
Evaluation Results
| Method | Links | |
|---|---|---|
| LUNAR2026.03 | 0.5504 | |
| DRL2025.10 | 0.5416 | |
| LLM-DASVariant=DRL2025.10 | 0.5281 | |
| DisentAD2026.03 | 0.5125 | |
| ICL2025.10 | 0.5097 | |
| LLM-DASVariant=PCA2025.10 | 0.5053 | |
| DRL2026.03 | 0.503 | |
| DTE2025.10 | 0.4974 | |
| DeepSVDD2025.10 | 0.4953 | |
| ECOD2025.10 | 0.4943 | |
| OFA-TAD2026.03 | 0.4912 | |
| NPT-AD2025.10 | 0.4888 | |
| LOF2026.03 | 0.4866 | |
| LOF2025.10 | 0.4866 | |
| DSVDD2026.03 | 0.4858 | |
| AutoEncoder2025.10 | 0.4833 | |
| KNN2026.03 | 0.4821 | |
| OCSVM2025.10 | 0.4803 | |
| AE2026.03 | 0.4799 | |
| MCM2026.03 | 0.4766 | |
| KNN2025.10 | 0.4737 | |
| iForest2026.03 | 0.4697 | |
| PCA2025.10 | 0.4678 | |
| IForest2025.10 | 0.4654 | |
| MCM2025.10 | 0.4631 | |
| DevNetlabeled_outliers=52025.12 | 0.431 | |
| DevNetlabeled outliers=52025.12 | 0.431 | |
| PReNetlabeled_outliers=52025.12 | 0.427 | |
| PReNetlabeled outliers=52025.12 | 0.427 | |
| FEAWADlabeled_outliers=52025.12 | 0.409 | |
| FEAWADlabeled outliers=52025.12 | 0.409 | |
| CODlabeled_outliers=52025.12 | 0.335 | |
| GDOFlabeled outliers=52025.12 | 0.332 | |
| ECODlabeled_outliers=52025.12 | 0.331 | |
| ECODlabeled outliers=52025.12 | 0.331 | |
| DeepSVDDlabeled_outliers=52025.12 | 0.328 | |
| DeepSVDDlabeled outliers=52025.12 | 0.328 | |
| WFRDAlabeled_outliers=52025.12 | 0.324 | |
| WFRDAlabeled outliers=52025.12 | 0.324 | |
| IForestlabeled_outliers=52025.12 | 0.319 | |
| DeepSADlabeled_outliers=52025.12 | 0.319 | |
| DeepSADlabeled outliers=52025.12 | 0.319 | |
| LUNARlabeled outliers=52025.12 | 0.314 | |
| SODlabeled_outliers=52025.12 | 0.307 | |
| AnoLLMModel Scale=360M2025.10 | 0.302 | |
| AnoLLMModel Scale=35M2025.10 | 0.301 | |
| DIFlabeled outliers=52025.12 | 0.291 | |
| REPENlabeled_outliers=52025.12 | 0.287 | |
| REPENlabeled outliers=52025.12 | 0.287 |