A Margin-Maximizing Fine-Grained Ensemble Method

TL;DR

This paper presents a novel ensemble method that optimizes a small number of classifiers by maximizing margins and adaptively allocating confidence, outperforming larger ensembles in resource-constrained settings.

Contribution

It introduces a margin-maximizing ensemble approach with a learnable confidence matrix and a novel margin-based loss function, improving efficiency and generalization.

Findings

Outperforms traditional random forests with fewer learners

Achieves higher accuracy than state-of-the-art ensemble methods

Demonstrates effective margin maximization and confidence adaptation

Abstract

Ensemble learning has achieved remarkable success in machine learning, but its reliance on numerous base learners limits its application in resource-constrained environments. This paper introduces an innovative "Margin-Maximizing Fine-Grained Ensemble Method" that achieves performance surpassing large-scale ensembles by meticulously optimizing a small number of learners and enhancing generalization capability. We propose a novel learnable confidence matrix, quantifying each classifier's confidence for each category, precisely capturing category-specific advantages of individual learners. Furthermore, we design a margin-based loss function, constructing a smooth and partially convex objective using the logsumexp technique. This approach improves optimization, eases convergence, and enables adaptive confidence allocation. Finally, we prove that the loss function is Lipschitz continuous, based on which we develop an efficient gradient optimization algorithm that simultaneously maximizes margins and dynamically adjusts learner weights. Extensive experiments demonstrate that our method outperforms traditional random forests using only one-tenth of the base learners and other state-of-the-art ensemble methods.