Stable Bagging

Stable bagging, an ensemble learning technique, aims to improve the accuracy and stability of machine learning models by training multiple models on bootstrapped subsets of the data and aggregating their predictions. Current research explores its application in diverse areas, including imbalanced datasets, robotic manipulation of deformable objects, and anomaly detection, often combining bagging with other techniques like stacking or boosting to enhance performance. This approach offers significant advantages in handling noisy data, improving generalization, and creating more robust and efficient models across various applications, from medical diagnosis to industrial automation.