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With the surge in data generation, both vertically (i.e., volume of data) andhorizontally (i.e., dimensionality), the burden of the curse of dimensionalityhas become increasingly palpable. Feature selection, a key facet ofdimensionality reduction techniques, has advanced considerably to address thischallenge. One such advancement is the Boruta feature selection algorithm,which successfully discerns meaningful features by contrasting them to theirpermutated counterparts known as shadow features. However, the significance ofa feature is shaped more by the data's overall traits than by its intrinsicvalue, a sentiment echoed in the conventional Boruta algorithm where shadowfeatures closely mimic the characteristics of the original ones. Building onthis premise, this paper introduces an innovative approach to the Borutafeature selection algorithm by incorporating noise into the shadow variables.Drawing parallels from the perturbation analysis framework of artificial neuralnetworks, this evolved version of the Boruta method is presented. Rigoroustesting on four publicly available benchmark datasets revealed that thisproposed technique outperforms the classic Boruta algorithm, underscoring itspotential for enhanced, accurate feature selection.

Noise-Augmented Boruta: The Neural Network Perturbation Infusion with Boruta Feature Selection