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

TL;DR

Hellsemble introduces a novel, interpretable ensemble framework that incrementally partitions data by difficulty, improving accuracy and efficiency in binary classification tasks.

Contribution

The paper presents Hellsemble, a new ensemble method that uses dataset difficulty to train specialized models and a router for efficient instance assignment.

Findings

Hellsemble outperforms classical ensemble methods on benchmark datasets.

It maintains computational efficiency and interpretability.

The approach effectively handles heterogeneous data distributions.

Abstract

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.