Hyperbox-based machine learning algorithms: a comprehensive survey

Khuat, TT; Ruta, D; Gabrys, B

Hyperbox-based machine learning algorithms: a comprehensive survey

Khuat, TT Ruta, D Gabrys, B

Permalink

Publisher:: Springer
Publication Type:: Journal Article
Citation:: Soft Computing, 2021, 25, (2), pp. 1325-1363
Issue Date:: 2021-01-01

In Progress

	Filename	Description	Size
	1901.11303v3.pdf	Accepted Manuscript	1.59 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is being processed and is not currently available.

Full metadata record

Field	Value	Language
dc.contributor.author	Khuat, TT
dc.contributor.author	Ruta, D
dc.contributor.author	Gabrys, B https://orcid.org/0000-0002-0790-2846
dc.date.accessioned	2021-05-04T02:51:54Z
dc.date.available	2021-05-04T02:51:54Z
dc.date.issued	2021-01-01
dc.identifier.citation	Soft Computing, 2021, 25, (2), pp. 1325-1363
dc.identifier.issn	1432-7643
dc.identifier.issn	1433-7479
dc.identifier.uri	http://hdl.handle.net/10453/148602
dc.description.abstract	© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. With the rapid development of digital information, the data volume generated by humans and machines is growing exponentially. Along with this trend, machine learning algorithms have been formed and evolved continuously to discover new information and knowledge from different data sources. Learning algorithms using hyperboxes as fundamental representational and building blocks are a branch of machine learning methods. These algorithms have enormous potential for high scalability and online adaptation of predictors built using hyperbox data representations to the dynamically changing environments and streaming data. This paper aims to give a comprehensive survey of the literature on hyperbox-based machine learning models. In general, according to the architecture and characteristic features of the resulting models, the existing hyperbox-based learning algorithms may be grouped into three major categories: fuzzy min–max neural networks, hyperbox-based hybrid models and other algorithms based on hyperbox representations. Within each of these groups, this paper shows a brief description of the structure of models, associated learning algorithms and an analysis of their advantages and drawbacks. Main applications of these hyperbox-based models to the real-world problems are also described in this paper. Finally, we discuss some open problems and identify potential future research directions in this field.
dc.language	English
dc.publisher	Springer
dc.relation.ispartof	Soft Computing
dc.relation.isbasedon	10.1007/s00500-020-05226-7
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.rights	This is a post-peer-review, pre-copyedit version of an article published in [Soft Computing]. The final authenticated version is available online at: https://link.springer.com/article/10.1007/s00500-020-05226-7]”
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Hyperbox-based machine learning algorithms: a comprehensive survey
dc.type	Journal Article
utslib.citation.volume	25
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	1702 Cognitive Sciences
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	in_progress	*
pubs.consider-herdc	false
dc.date.updated	2021-05-04T02:51:47Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	25
utslib.citation.issue	2

Abstract:

© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. With the rapid development of digital information, the data volume generated by humans and machines is growing exponentially. Along with this trend, machine learning algorithms have been formed and evolved continuously to discover new information and knowledge from different data sources. Learning algorithms using hyperboxes as fundamental representational and building blocks are a branch of machine learning methods. These algorithms have enormous potential for high scalability and online adaptation of predictors built using hyperbox data representations to the dynamically changing environments and streaming data. This paper aims to give a comprehensive survey of the literature on hyperbox-based machine learning models. In general, according to the architecture and characteristic features of the resulting models, the existing hyperbox-based learning algorithms may be grouped into three major categories: fuzzy min–max neural networks, hyperbox-based hybrid models and other algorithms based on hyperbox representations. Within each of these groups, this paper shows a brief description of the structure of models, associated learning algorithms and an analysis of their advantages and drawbacks. Main applications of these hyperbox-based models to the real-world problems are also described in this paper. Finally, we discuss some open problems and identify potential future research directions in this field.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/148602