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
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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.
Conditional inference forests rank competitively as top-k feature selectors in classification and regression benchmarks, with runtime factors identified but limited impact on scores.
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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.