Using pyod tools to detect outlier in anomaly detection benchmarks
Anomaly Detection Meta-Analysis Benchmarks
Python Outlier Detection (PyOD)
you can simply clone this repository and run
jupyter notebook outlier_det_imgseg.ipynb/outlier_det_wine.ipynb
There are imgseg/wine results, more detail can be found at results folder and jupyter notebook.
This is sample result, PCA+KNN performs best(roc 0.87) and PCA performs worst(roc 0.57)
This is all benchmarks result, KNN and LOF performs well in low anomaly rate but badly in high anomaly. On the contrast, PCA and LODA are more robust.
This is sample result, also, PCA+KNN performs best(roc 0.87).
This is all benchmarks result, the trend of results of all models is consistent.
