Anomaly Detection on shuttle
1AUCAnoLLM
Evaluation results
| Method | Links | ||
|---|---|---|---|
| AnoLLMModel Size=135M2025.10 | 1 | — | |
| AnoLLMModel Size=360M2025.10 | 1 | — | |
| LLM-DASMethod Variant=DRL2025.10 | 0.9999 | — | |
| NeuTraLAD2026.02 | 0.9994 | — | |
| DTE2026.02 | 0.9993 | — | |
| CTADBase Detector=MCM2026.02 | 0.9993 | — | |
| DTE2025.10 | 0.9993 | — | |
| DRL2025.10 | 0.9992 | — | |
| DRL2026.02 | 0.9989 | — | |
| CTADBase Detector=DRL2026.02 | 0.9989 | — | |
| LOF2025.10 | 0.9983 | — | |
| ECOD2026.02 | 0.9978 | — | |
| ECOD2025.10 | 0.9978 | — | |
| CTADBase Detector=KNN2026.02 | 0.9977 | — | |
| MCM2026.02 | 0.9975 | — | |
| MCM2025.10 | 0.9975 | — | |
| IFsamples=490972025.07 | 0.997 | — | |
| OCSVM2026.02 | 0.9969 | — | |
| OCSVM2025.10 | 0.9969 | — | |
| IForest2026.02 | 0.9961 | — | |
| IForest2025.10 | 0.9961 | — | |
| DeepSVDD2026.02 | 0.9952 | — | |
| DeepSVDD2025.10 | 0.9952 | — | |
| AutoEncoder2026.02 | 0.9944 | — | |
| AutoEncoder2025.10 | 0.9944 | — | |
| PCA2026.02 | 0.9936 | — | |
| PCA2025.10 | 0.9936 | — | |
| ICL2026.02 | 0.9935 | — | |
| ICL2025.10 | 0.9935 | — | |
| MCMMethod Category=Task-Specific2025.12 | 0.9931 | — | |
| NPT-AD2025.10 | 0.9931 | — | |
| ECODsamples=490972025.07 | 0.992 | — | |
| UniODsamples=490972025.07 | 0.992 | — | |
| ESADratio of labeled anomalies=0.012020.12 | 0.991 | — | |
| GOAD2026.02 | 0.9897 | — | |
| KNN2026.02 | 0.9893 | — | |
| KNN2025.10 | 0.9893 | — | |
| ICAD-LLMMethod Category=Task-Specific2025.12 | 0.9874 | — | |
| Deep SADratio of labeled anomalies=0.012020.12 | 0.984 | — | |
| SS-DGMratio of labeled anomalies=0.012020.12 | 0.979 | — | |
| SSAD Hybridratio of labeled anomalies=0.012020.12 | 0.977 | — | |
| GOADMethod Category=Task-Specific2025.12 | 0.9607 | — | |
| ICAD-LLMMethod Category=Universal2025.12 | 0.9534 | — | |
| DIF2022.06 | 0.941 | 0.15 | |
| IForestMethod Category=Task-Specific2025.12 | 0.9318 | — | |
| ACRMethod Category=Universal2025.12 | 0.9224 | — | |
| ICAD-LLMModality=Tabular, Generalization (Unseen Datasets)=true2025.12 | 0.9164 | — | |
| ACRModality=Tabular, Generalization (Unseen Datasets)=true2025.12 | 0.9137 | — | |
| UniADMethod Category=Universal2025.12 | 0.9075 | — | |
| DAGMMMethod Category=Task-Specific2025.12 | 0.9011 | — | |
| NeuTraL ADMethod Category=Universal2025.12 | 0.8814 | — | |
| UniADModality=Tabular, Generalization (Unseen Datasets)=true2025.12 | 0.8707 | — | |
| PID2022.06 | 0.864 | 0.059 | |
| Deep SVDDratio of labeled anomalies=0.012020.12 | 0.863 | — | |
| IF2022.06 | 0.862 | 0.075 | |
| EIF2022.06 | 0.843 | 0.061 | |
| NeuTralADModality=Tabular, Generalization (Unseen Datasets)=true2025.12 | 0.8375 | — | |
| LeSiNN2022.06 | 0.805 | 0.048 | |
| kNNsamples=490972025.07 | 0.658 | — | |
| DTE-NPsamples=490972025.07 | 0.625 | — | |
| LLM-DASMethod Variant=PCA2025.10 | 0.402 | — | |
| AE2026.03 | — | 0.9683 | |
| AnoLLMModel Scale=35M2025.10 | — | 0.997 | |
| AnoLLMModel Scale=360M2025.10 | — | 0.996 | |
| AutoEncoder2025.10 | — | 0.9316 | |
| DeepSVDD2025.10 | — | 0.9818 | |
| DisentAD2026.03 | — | 0.9958 | |
| DRL2026.03 | — | 0.9693 | |
| DRL2025.10 | — | 0.9819 | |
| DSVDD2026.03 | — | 0.9622 | |
| DTE2025.10 | — | 0.9403 | |
| ECOD2025.10 | — | 0.9815 | |
| ICL2025.10 | — | 0.9811 | |
| iForest2026.03 | — | 0.9862 | |
| IForest2025.10 | — | 0.9172 | |
| KNN2026.03 | — | 0.9225 | |
| KNN2025.10 | — | 0.937 | |
| LLM-DASVariant=PCA2025.10 | — | 0.9906 | |
| LLM-DASVariant=DRL2025.10 | — | 0.9991 | |
| LOF2026.03 | — | 0.9458 | |
| LOF2025.10 | — | 0.9601 | |
| LUNAR2026.03 | — | 0.8138 | |
| MCM2026.03 | — | 0.9683 | |
| MCM2025.10 | — | 0.9479 | |
| NPT-AD2025.10 | — | 0.9151 | |
| OCSVM2025.10 | — | 0.9488 | |
| OFA-TAD2026.03 | — | 0.9961 | |
| PCA2025.10 | — | 0.9627 |