Abstract

High-dimensional biological datasets present significant challenges for accurate predictive modeling due to the curse of dimensionality and the presence of irrelevant or redundant features. This paper introduces a novel adaptive ensemble learning framework that incorporates dynamic feature selection to enhance predictive accuracy in such datasets. The proposed method combines multiple base learners with a dynamically adjusted weighting scheme, informed by the performance of each learner on subsets of features selected using a novel hybrid feature selection strategy. This strategy integrates filter, wrapper, and embedded methods to identify the most relevant feature subsets for each base learner. The adaptive weighting mechanism dynamically adjusts the contribution of each base learner based on its performance on a validation set. We evaluate the performance of the proposed method on several benchmark biological datasets, demonstrating its superiority over existing ensemble learning and feature selection techniques. Results show a significant improvement in predictive accuracy, robustness, and interpretability, making it a promising tool for analyzing complex biological data.