Adaptive Ensemble Learning with Confidence Bounds

TL;DR

This paper introduces Hedged Bandits, an ensemble learning method that provides both performance guarantees and adaptive, data-driven predictions in distributed, high-dimensional data environments.

Contribution

It presents a novel ensemble learning framework with performance guarantees and adaptability, addressing limitations of existing meta-learning techniques.

Findings

Outperforms existing ensemble methods in medical informatics tasks.

Provides both asymptotic and rate of learning guarantees.

Adapts predictions effectively based on data.

Abstract

Extracting actionable intelligence from distributed, heterogeneous, correlated and high-dimensional data sources requires run-time processing and learning both locally and globally. In the last decade, a large number of meta-learning techniques have been proposed in which local learners make online predictions based on their locally-collected data instances, and feed these predictions to an ensemble learner, which fuses them and issues a global prediction. However, most of these works do not provide performance guarantees or, when they do, these guarantees are asymptotic. None of these existing works provide confidence estimates about the issued predictions or rate of learning guarantees for the ensemble learner. In this paper, we provide a systematic ensemble learning method called Hedged Bandits, which comes with both long run (asymptotic) and short run (rate of learning) performance guarantees. Moreover, our approach yields performance guarantees with respect to the optimal local prediction strategy, and is also able to adapt its predictions in a data-driven manner. We illustrate the performance of Hedged Bandits in the context of medical informatics and show that it outperforms numerous online and offline ensemble learning methods.