Recursive Feature

Recursive Feature Elimination (RFE) is a feature selection technique that iteratively removes less important features from a dataset to improve model performance and reduce dimensionality. Current research focuses on enhancing RFE's effectiveness, particularly for complex models like neural networks, by incorporating techniques like conformal prediction to improve confidence in feature selection and by combining it with other filter methods to improve efficiency and accuracy in high-dimensional datasets. These advancements are significant because they address challenges in handling noisy data and improve the interpretability and efficiency of machine learning models across various applications, including network intrusion detection and sentiment analysis.