GPU versions of Boruta run substantially faster than the original CPU implementation while maintaining comparable feature selection accuracy on tested datasets.
Feature selection with the Boruta package
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
method 2
citation-polarity summary
years
2026 2roles
method 2polarities
use method 2representative citing papers
Quantum-chemical bonding descriptors improve machine learning predictions of materials properties and enable symbolic regression to recover intuitive expressions for force constants and thermal conductivity.
citing papers explorer
-
Novel GPU Boruta algorithms for feature selection from high-dimensional data
GPU versions of Boruta run substantially faster than the original CPU implementation while maintaining comparable feature selection accuracy on tested datasets.
-
A critical assessment of bonding descriptors for predicting materials properties
Quantum-chemical bonding descriptors improve machine learning predictions of materials properties and enable symbolic regression to recover intuitive expressions for force constants and thermal conductivity.