Skip to content

RADE is a resource-efficient decision tree ensemble method (DTEM) based anomaly detection approach that augments standard DTEM classifiers resulting in competitive anomaly detection capabilities and significant savings in resource usage.

License

Notifications You must be signed in to change notification settings

vmware-labs/efficient-supervised-anomaly-detection

Repository files navigation

RADE scikit Classifier (v1.0)

RADE is a resource-efficient decision tree ensemble method (DTEM) based anomaly detection approach that augments standard DTEM classifiers resulting in competitive anomaly detection capabilities and significant savings in resource usage.

The current implementation of RADE augments either Random-Forest or XGBoost.

More information about RADE can be found in:
RADE: resource‑efficient supervised anomaly detection using decision tree‑based ensemble methods (Springer ML)

Files:

rade_classifier.py - RADE sci-kit classifier

example_program.py - Basic comparison example between RF, XGBoost, and RADE

Prerequisities:

numpy
pandas
sklearn
xgboost

or alternatively run:
$ pip3 install -r requirements.txt

For more information, support and advanced examples contact:
Yaniv Ben-Itzhak, ybenitzhak@vmware.com
Shay Vargaftik, shayv@vmware.com

About

RADE is a resource-efficient decision tree ensemble method (DTEM) based anomaly detection approach that augments standard DTEM classifiers resulting in competitive anomaly detection capabilities and significant savings in resource usage.

Resources

License

Code of conduct

Security policy

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages