| Citekey | HaririEtAl2019Extended |
| Source Code | https://github.com/sahandha/eif |
| Learning type | unsupervised |
| Input dimensionality | multivariate |
The output will be an anomaly score for every input data point
- python 3
- numpy
- cython
- pip
- eif
@ARTICLE{8888179, author={S. {Hariri} and M. {Carrasco Kind} and R. J. {Brunner}}, journal={IEEE Transactions on Knowledge and Data Engineering}, title={Extended Isolation Forest}, year={2019}, volume={}, number={}, pages={1-1}, keywords={Forestry;Vegetation;Distributed databases;Anomaly detection;Standards;Clustering algorithms;Heating systems;Anomaly Detection;Isolation Forest}, doi={10.1109/TKDE.2019.2947676}, ISSN={}, month={},}