HNPclassifier: Hierarchical Neyman-Pearson Classification for Ordered Classes
The Hierarchical Neyman-Pearson (H-NP) classification framework
extends the Neyman-Pearson classification paradigm to multi-class settings
where classes have a natural priority ordering. This is particularly useful
for classification in unbalanced dataset, for example, disease severity
classification, where under-classification errors (misclassifying patients
into less severe categories) are more consequential than other
misclassifications. The package implements H-NP umbrella algorithms that
controls under-classification errors under user specified control levels
with high probability. It supports the creation of H-NP classifiers using
scoring functions based on built-in classification methods (including
logistic regression, support vector machines, and random forests), as well
as user-trained scoring functions.
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=HNPclassifier
to link to this page.