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