Why imprecise regression: A discussion

Prade, H; Serrurier, M

Why imprecise regression: A discussion

Prade, H

Serrurier, M

Permalink

Publication Type:: Conference Proceeding
Citation:: Advances in Intelligent and Soft Computing, 2010, 77 pp. 527 - 535
Issue Date:: 2010-12-01

Closed Access

	Filename	Description	Size
	2013007894OK.pdf	Published version	968.28 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Prade, H https://orcid.org/0000-0003-4586-8527	en_US
dc.contributor.author	Serrurier, M	en_US
dc.date.issued	2010-12-01	en_US
dc.identifier.citation	Advances in Intelligent and Soft Computing, 2010, 77 pp. 527 - 535	en_US
dc.identifier.isbn	9783642147456	en_US
dc.identifier.issn	1867-5662	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32910
dc.description.abstract	Machine learning, and more specifically regression, usually focuses on the search for a precise model, when precise data are available. It is well-known that the model thus found may not exactly describe the target concept, due to the existence of learning bias. In order to overcome the problem of learning models having an illusory precision, a so-called imprecise regression method has been recently proposed for non-fuzzy data. The goal of imprecise regression is to find a model that offers a good trade-off between faithfulness w.r.t. data and (meaningful) precision. In this paper, we propose an improved version of the initial approach. The interest of such an approach with respect to classical regression is discussed in the perspective of coping with learning bias. This approach is also contrasted with other fuzzy regression approaches. © 2010 Springer-Verlag Berlin Heidelberg.	en_US
dc.relation.ispartof	Advances in Intelligent and Soft Computing	en_US
dc.relation.isbasedon	10.1007/978-3-642-14746-3-65	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Why imprecise regression: A discussion	en_US
dc.type	Conference Proceeding
utslib.citation.volume	77	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
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/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	77	en_US

Abstract:

Machine learning, and more specifically regression, usually focuses on the search for a precise model, when precise data are available. It is well-known that the model thus found may not exactly describe the target concept, due to the existence of learning bias. In order to overcome the problem of learning models having an illusory precision, a so-called imprecise regression method has been recently proposed for non-fuzzy data. The goal of imprecise regression is to find a model that offers a good trade-off between faithfulness w.r.t. data and (meaningful) precision. In this paper, we propose an improved version of the initial approach. The interest of such an approach with respect to classical regression is discussed in the perspective of coping with learning bias. This approach is also contrasted with other fuzzy regression approaches. © 2010 Springer-Verlag Berlin Heidelberg.

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

http://hdl.handle.net/10453/32910