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SotA/Methodology/Anomaly Detection/Vehicle Claims

Anomaly Detection on Vehicle Claims

Metric: AUC (higher is better)

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Results

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#Model↕AUC▼AugmentationsPaperDate↕Code
1Random Forest98.65NoUnsupervised Anomaly Detection for Auditing Data...2022-10-25Code
2Gradient Boosting95.88NoUnsupervised Anomaly Detection for Auditing Data...2022-10-25Code
3SOM65.43NoUnsupervised Anomaly Detection for Auditing Data...2022-10-25Code
4Isolation Forest59.42NoUnsupervised Anomaly Detection for Auditing Data...2022-10-25Code
5Latent Outlier Exposure58.59NoUnsupervised Anomaly Detection for Auditing Data...2022-10-25Code
6NeuTraL-AD57.03NoUnsupervised Anomaly Detection for Auditing Data...2022-10-25Code
7RSRAE55.38NoUnsupervised Anomaly Detection for Auditing Data...2022-10-25Code
8SOM-DAGMM53.82NoUnsupervised Anomaly Detection for Auditing Data...2022-10-25Code
9Local Outlier Factor52.86NoUnsupervised Anomaly Detection for Auditing Data...2022-10-25Code
10One Class Support Vector Machines51.68NoUnsupervised Anomaly Detection for Auditing Data...2022-10-25Code
11DAGMM51.22NoUnsupervised Anomaly Detection for Auditing Data...2022-10-25Code