按难易程度分层训练模型，提升分类效率与可解释性
Divide, Specialize, and Route: A New Approach to Efficient Ensemble Learning
- 将数据按错误率分圈递进训练，每层专注更难样本
- 在OpenML-CC18和Tabzilla上准确率超越传统集成方法
- 适合追求高效且可解释的分类场景
集成学习在提升预测性能方面表现优异，但传统方法如自助采样、提升和动态集成选择（DES）存在计算成本高、难以适应异构数据分布的问题。为此，我们提出Hellsemble，一种新颖且可解释的二分类集成框架，利用数据集复杂度在训练和推理阶段进行优化。Hellsemble通过迭代传递被误分类的实例，将数据集逐步划分为难度递增的若干圈，形成一组专业化的基础学习器。每个模型在越来越难的子集上训练，同时一个独立的路由模型学习根据推断出的难度将新样本分配给最合适的基模型。实验结果表明，Hellsemble在保持计算高效和可解释性的同时，实现了强分类准确率。在OpenML-CC18和Tabzilla基准测试中，其性能常优于经典集成方法。研究发现，引入实例级难度有助于构建更高效、更鲁棒的集成系统。
原文摘要 · Abstract (English)
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.
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