Random Hyperboxes

Khuat, TT; Gabrys, B

Random Hyperboxes

Khuat, TT Gabrys, B

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Publication Type:: Journal Article
Citation:: 2020
Issue Date:: 2020-06-01

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Field	Value	Language
dc.contributor.author	Khuat, TT
dc.contributor.author	Gabrys, B https://orcid.org/0000-0002-0790-2846
dc.date.accessioned	2021-05-04T01:48:01Z
dc.date.available	2021-05-04T01:48:01Z
dc.date.issued	2020-06-01
dc.identifier.citation	2020
dc.identifier.uri	http://hdl.handle.net/10453/148596
dc.description.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.
dc.language	en
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	Random Hyperboxes
dc.type	Journal Article
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	recently_added	*
dc.date.updated	2021-05-04T01:47:58Z

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.

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http://hdl.handle.net/10453/148596