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