标题： 划分、专业化和路由：一种高效的集成学习新方法 显示英文标题

标题： Divide, Specialize, and Route: A New Approach to Efficient Ensemble Learning

摘要： 集成学习已被证明在提升预测性能方面是有效的，但传统的方法如装袋、提升和动态集成选择（DES）存在计算成本高和对异构数据分布适应性有限的问题。 为了解决这些限制，我们提出了Hellsemble，这是一种新颖且可解释的二分类集成框架，在训练和推理过程中利用数据集的复杂性。 Hellsemble通过将被错误分类的实例从简单的模型迭代传递到后续模型，逐步将数据集划分为不同难度的圈，形成一个专业基础学习者的委员会。 每个模型都在越来越具有挑战性的子集上进行训练，同时一个单独的路由器模型学习根据推断出的难度将新实例分配给最合适的基模型。 Hellsemble在保持计算效率和可解释性的同时实现了强大的分类准确性。 在OpenML-CC18和Tabzilla基准上的实验结果表明，Hellsemble通常优于经典集成方法。 我们的研究结果表明，接受实例级别的难度为构建高效且稳健的集成系统提供了一个有前景的方向。 显示英文摘要

摘要： Ensemble learning has proven effective in boosting predictive performance, but traditional methods such as bagging, boosting, and dynamic ensemble selection (DES) suffer from high computational cost and limited adaptability to heterogeneous data distributions. To address these limitations, we propose Hellsemble, a novel and interpretable ensemble framework for binary classification that leverages dataset complexity during both training and inference. Hellsemble incrementally partitions the dataset into circles of difficulty by iteratively passing misclassified instances from simpler models to subsequent ones, forming a committee of specialised base learners. Each model is trained on increasingly challenging subsets, while a separate router model learns to assign new instances to the most suitable base model based on inferred difficulty. Hellsemble achieves strong classification accuracy while maintaining computational efficiency and interpretability. Experimental results on OpenML-CC18 and Tabzilla benchmarks demonstrate that Hellsemble often outperforms classical ensemble methods. Our findings suggest that embracing instance-level difficulty offers a promising direction for constructing efficient and robust ensemble systems.