KDD Cup
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Paper-Author Check | KDD Cup | AUC0.8734 | 19 | |
| Partial Dependence Plot (PDP) | KDD Cup | Runtime5.5 | 19 | |
| Time-series forecasting | KDD Cup H 2018 | MASE0.952 | 10 | |
| Customer Relationship Management | KDD Cup Direct Mailing RNN Simulator 1998 1.0 (test) | Average Reward ($)9.66 | 10 | |
| Time-series forecasting | KDD Cup 2018 (test) | MSE0.67 | 10 | |
| Path-Dependent SHAP | KDD Cup 1999 | Runtime3.3 | 10 | |
| Time-series Forecasting | KDD-cup | NRMSE (sum)0.2605 | 10 | |
| Background SHAP | KDD Cup 1999 | Runtime3 | 8 | |
| Joint Partial Dependence Plot (Joint-PDP) | KDD Cup | Runtime9 | 7 | |
| Time Series Anomaly Detection | KDD-Cup99 | AUC98 | 6 | |
| Any-Order Partial Dependence Interdependence Values (Any-Order-PDIVs) | KDD Cup | Runtime15 | 6 | |
| Intrusion Detection | KDD Cup 1999 (test) | Accuracy99.6902 | 6 | |
| Intrusion Detection | KDD Cup 1999 (train) | Accuracy99.9734 | 6 | |
| Time Series Forecasting | KDD Cup | MSE0.8341 | 5 | |
| Binary classification (normal vs abnormal) | KDD Cup subsets (train) | Accuracy100 | 4 | |
| 3-class classification (normal vs abnormal vs mix) | KDD Cup subsets (train) | Accuracy99.4 | 2 | |
| Dimensionality Reduction | KDD Cup99 4M | Centroid Triplet Accuracy57.2 | 2 | |
| Intrusion Detection | KDD Cup 99 | Accuracy97.99 | 1 | |
| Classification | KDD CUP 99 | Accuracy95.6 | 1 | |
| Dimensionality Reduction | KDD Cup 4M 99 | Metric- | 0 |
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