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Lifecycle: experimental

raedda

Model-based framework for robust classification that jointly accounts for outliers, label noise and unobserved classes in the test set, employing a MVN mixture model with Parsimonious structure.

This repository is associated with the paper Cappozzo, Greselin, Murphy (2020) Anomaly and Novelty detection for robust semi-supervised learning. https://doi.org/10.1007/s11222-020-09959-1

Installation

You can install the development version of raedda from github with:

devtools::install_github("AndreaCappozzo/raedda")

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Model-based framework for robust classification that jointly accounts for outliers, label noise and unobserved classes in the test set.

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