Frontiers

Advancing standardized cattle behavior classification with a random forest model evaluated across diverse datasets

The lack of precise, autonomous tools for monitoring and classifying cattle behavior limits farmers’ ability to make proactive and informed decisions regarding grazing and herd management. Currently, ...
GitHub

Tabular Diffusion and Flow-Matching (AISTATS 2024)

This repo contains the official implementation of the AISTATS 2024 paper Generating and Imputing Tabular Data via Diffusion and Flow-based XGBoost Models. To make it easily accessible, we release our ...
Frontiers

Causative Classification of Ischemic Stroke by the Machine Learning Algorithm Random Forests

Prognosis, recurrence rate, and secondary prevention strategies differ by different etiologies in acute ischemic stroke. However, identifying its cause is challenging. We retrospectively reviewed the ...
GitHub

Scikit-Learn compatible HMM and DTW based sequence machine learning algorithms in Python.

Sequentia is a Python package that provides various classification and regression algorithms for sequential data, including methods based on hidden Markov models and dynamic time warping. Some ...