Model Combination

Model combination, also known as ensemble learning, aims to improve prediction accuracy and robustness by integrating the outputs of multiple individual models. Current research focuses on developing effective combination strategies, including linear and non-linear methods, and exploring diverse model architectures such as neural networks (e.g., LSTM, convolutional networks), tree-based models, and Bayesian approaches. This field is significant because it enhances the performance of machine learning across various applications, from financial time series forecasting and medical diagnosis to autonomous vehicle navigation and educational technology, by leveraging the strengths of multiple models while mitigating individual weaknesses.