Ensemble Feature

Ensemble feature selection aims to improve the accuracy and stability of machine learning models by combining the results of multiple feature selection methods. Current research focuses on developing novel ensemble algorithms, such as hierarchical stacking and those incorporating clustering or data-driven thresholding, to handle high-dimensional, correlated data and improve the robustness of feature selection in diverse applications. This approach is proving valuable across various fields, including disease prediction (e.g., cancer, Alzheimer's), time series forecasting, and paralinguistics, leading to more reliable and efficient models for complex prediction tasks. The resulting improved feature sets enhance model interpretability and contribute to more accurate and stable predictions.