Robust Explainable Outlier Detection Based on OutlierTree

Bagged OutlierTrees is an explainable unsupervised outlier detection method based on an ensemble implementation of the existing OutlierTree procedure (Cortes, 2020). This implementation takes advantage of bootstrap aggregating (bagging) to improve robustness by reducing the possible masking effect and subsequent high variance (similarly to Isolation Forest), hence the name "Bagged OutlierTrees". To learn more about the base procedure OutlierTree (Cortes, 2020), please refer to .


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install.packages("bagged.outliertrees")

1.0.0 by Rafael Santos, 25 days ago


https://github.com/RafaJPSantos/bagged.outliertrees


Report a bug at https://github.com/RafaJPSantos/bagged.outliertrees/issues


Browse source code at https://github.com/cran/bagged.outliertrees


Authors: Rafael Santos [aut, cre]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports outliertree, dplyr, doSNOW, parallel, foreach, rlist, data.table


See at CRAN