Decision Tree & Ensemble Learning for Imbalanced Data
Bhattachryya and Hellinger distance as Python functions
Fixed-size sketches of probability distributions: Bhattacharyya coefficient and Hellinger distance from ~1 KB per profile, with error independent of alphabet size. Library + fully verified paper (every number traced to a committed script).
HF-HMA: an open research experiment in Hellinger-medoid aggregation for finite candidate policies with noisy feedback.
A research-oriented experimental framework for imbalanced classification using Hellinger Distance Decision Trees, Bagging, AdaBoost, and SMOTE.
R package for entropy-informed detection of emerging viral variants and genomic surveillance. Implements per-site Shannon entropy, Gaussian mixture model site selection, Hellinger distance, and non-parametric change-point detection. Validated on SARS-CoV-2 Spike protein sequences from NCBI and GISAID databases.
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