Multivariate Outlier Detection and Replacement

Provides a random forest based implementation of the method described in Chapter 7.1.2 (Regression model based anomaly detection) of Chandola et al. (2009) . It works as follows: Each numeric variable is regressed onto all other variables by a random forest. If the scaled absolute difference between observed value and out-of-bag prediction of the corresponding random forest is suspiciously large, then a value is considered an outlier. The package offers different options to replace such outliers, e.g. by realistic values found via predictive mean matching. Once the method is trained on a reference data, it can be applied to new data.


Reference manual

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0.1.1 by Michael Mayer, 10 months ago

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Authors: Michael Mayer [aut, cre]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports stats, graphics, FNN, ranger, missRanger

Suggests dplyr, knitr, rmarkdown

See at CRAN