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Random Hyperboxes
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DOI：10.1109/TNNLS.2021.3104896.png)
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Abstract
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This article 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 (FMNNs), 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.
Keywords:
Training
Neural networks
Machine learning algorithms
Correlation
Boosting
Prediction algorithms
Buildings
Classification
ensemble learning
general fuzzy min-max neural network (GFMMNN)
random hyperboxes (RH)
randomization-based learning
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8.9
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7.6K
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7.2W
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