Heterogeneous Ensemble

Heterogeneous ensembles combine diverse machine learning models—ranging from simple algorithms to complex deep neural networks—to improve prediction accuracy and robustness compared to using a single model. Current research focuses on optimizing ensemble construction, including exploring different model architectures and feature extraction methods, and developing efficient inference systems for these computationally intensive approaches. This technique finds applications across various fields, from improving recommendation systems and object detection to enhancing medical image analysis and materials design, demonstrating its broad utility in tackling complex real-world problems.