Random Hyperboxes

TL;DR

This paper introduces Random Hyperboxes, an ensemble classifier based on hyperbox models trained on random subsets, demonstrating superior performance and providing theoretical error bounds, with extensive empirical validation.

Contribution

The paper presents a novel ensemble classifier called Random Hyperboxes, combining hyperbox models trained on random subsets, and offers a generalization error bound analysis.

Findings

Outperforms other fuzzy min-max neural networks and popular algorithms.

Competitive with existing ensemble methods across 20 datasets.

Provides insights into generalization error bounds for real datasets.

Abstract

This paper proposes a simple yet powerful ensemble classifier, called Random Hyperboxes, constructed from individual hyperbox-based classifiers trained on the random subsets of sample and feature spaces of the training set. We also show a generalization error bound of the proposed classifier based on the strength of the individual hyperbox-based classifiers as well as the correlation among them. The effectiveness of the proposed classifier is analyzed using a carefully selected illustrative example and compared empirically with other popular single and ensemble classifiers via 20 datasets using statistical testing methods. The experimental results confirmed that our proposed method outperformed other fuzzy min-max neural networks, popular learning algorithms, and is competitive with other ensemble methods. Finally, we identify the existing issues related to the generalization error bounds of the real datasets and inform the potential research directions.