Leveraging Neural Networks and Calibration Measures for Confident Feature Selection

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. Building on this, this paper introduces NeuroBoruta, that extends the traditional Boruta approach by integrating neural networks and calibration metrics to improve prediction accuracy and reduce model uncertainty. By augmenting shadow features with noise and utilizing neural network-based perturbation for importance evaluation, and further incorporating calibration metrics alongside accuracy this evolved version of the Boruta method is presented. Experimental results demonstrate that NeuroBoruta significantly enhances the predictive performance and reliability of classification models across various datasets, including medical imaging and standard UCI datasets. This study underscores the importance of considering both feature relevance and model uncertainty in the feature selection process, particularly in domains requiring high accuracy and reliability.