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. For theoretical details, please refer to
Lijia Wang, Y. X. Rachel Wang, Jingyi Jessica Li & Xin Tong (2024) <doi:10.1080/01621459.2023.2270657>.
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