Random Hyperboxes
- Publication Type:
- Journal Article
- Citation:
- 2020
- Issue Date:
- 2020-06-01
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Filename | Description | Size | |||
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2006.00695v2.pdf | Accepted Manuscript | 596.61 kB |
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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.
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