Evolutionary Bagging

Evolutionary bagging is an ensemble learning technique that improves model performance by creating and iteratively refining diverse subsets (bags) of training data. Current research focuses on optimizing bag creation strategies, including adaptive algorithms that prioritize data homogeneity and methods leveraging evolutionary algorithms to dynamically adjust bag composition. This approach shows promise in handling imbalanced data, improving model efficiency (e.g., reducing the number of trees needed for comparable performance to Random Forests), and enhancing performance in various applications, such as data stream classification and unsupervised sentiment analysis. The resulting models often exhibit improved accuracy, robustness, and reduced computational costs compared to traditional bagging methods.