Random Hyperboxes
This work addresses classification accuracy for machine learning practitioners, offering an incremental improvement over existing ensemble methods.
The paper tackles the problem of improving classification accuracy by proposing Random Hyperboxes, an ensemble classifier built from hyperbox-based classifiers trained on random subsets of samples and features, and shows it outperforms other fuzzy min-max neural networks and popular learning algorithms, being competitive with other ensemble methods across 20 datasets.
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.