Dynamic Ensemble Learning: A Multidimensional Abilities Modeling Perspective

Ensemble learning is a powerful approach to improving model performance in addressing big data challenges. It first trains multiple base models with existing machine learning algorithms and then combines their outputs to yield the final prediction result. Despite the demonstrated potential of current ensemble strategies, their integration primarily relies on unidimensional metrics for each base model (e.g., prediction accuracy), which are too coarse-grained to adequately represent the multifaceted abilities of models. In this paper, we propose MADE, a novel Multidimensional Ability aware Dynamic Ensemble paradigm by drawing upon the fine-grained and well-developed measurement of human learning. Specifically, MADE incorporates a dynamic ensemble algorithm that modifies the models’ weights in accordance with their evolving abilities throughout the training process. To evaluate the multidimensional abilities of the base models, we develop a diagnostic module that captures individual base models’ latent knowledge levels. Besides, an ensemble weight inductor is designed in MADE to generate individual ensemble weights for each sample, different from previous ensemble methods that assign the same ensemble weights to all samples. Extensive experiments on diverse datasets demonstrate the effectiveness of our proposed model in achieving improved ensemble performance.