Diverse Ensemble

Diverse ensemble methods combine predictions from multiple models to improve accuracy, robustness, and uncertainty quantification in various machine learning tasks. Current research focuses on developing efficient ensemble techniques, including those based on AdaBoost, weight averaging, and contrastive learning, and applying them to diverse areas such as natural language processing, image classification, and time series forecasting. This approach is proving valuable for addressing challenges like limited training data, adversarial attacks, and concept drift, leading to improved performance and reliability in real-world applications.