Ayana Takai’s research while affiliated with Nagoya University and other places

... Traditional ensemble methods [15,16] often overlook variations in predictive performance among different models, limiting the potential benefits of ensemble learning. Zhao et al. [17] evaluated the predictive performance of each base model through in-sample testing and calculated the weight of each model. They then used these weights to train the model for weight prediction, and finally, calculated a weighted average to obtain the ensemble result. ...

Reference:

Deep-Reinforcement-Learning-Based Dynamic Ensemble Model for Stock Prediction