Cost-sensitive decision trees with multiple cost scales

Qin, Z; Zhang, S; Zhang, C

Cost-sensitive decision trees with multiple cost scales

Qin, Z Zhang, S Zhang, C

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 2004, 3339 pp. 380 - 390
Issue Date:: 2004-12-01

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Field	Value	Language
dc.contributor.author	Qin, Z	en_US
dc.contributor.author	Zhang, S	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2004-12-01	en_US
dc.identifier.citation	Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 2004, 3339 pp. 380 - 390	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/1774
dc.description.abstract	How to minimize misclassification errors has been the main focus of Inductive learning techniques, such as CART and C4.5. However, misclassification error is not the only error in classification problem. Recently, researchers have begun to consider both test and misclassification costs. Previous works assume the test cost and the misclassification cost must be defined on the same cost scale. However, sometimes we may meet difficulty to define the multiple costs on the same cost scale. In this paper, we address the problem by building a cost-sensitive decision tree by involving two kinds of cost scales, that minimizes the one kind of cost and control the other in a given specific budget. Our work will be useful for many diagnostic tasks involving target cost minimization and resource consumption for obtaining missing information. © Springer-Verlag Berlin Heidelberg 2004.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0449535
dc.relation.ispartof	Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Cost-sensitive decision trees with multiple cost scales	en_US
dc.type	Conference Proceeding
utslib.citation.volume	3339	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Cairns, Australia	en_US
dc.location.activity	Lugano, SWITZERLAND
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
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	3339	en_US

Abstract:

How to minimize misclassification errors has been the main focus of Inductive learning techniques, such as CART and C4.5. However, misclassification error is not the only error in classification problem. Recently, researchers have begun to consider both test and misclassification costs. Previous works assume the test cost and the misclassification cost must be defined on the same cost scale. However, sometimes we may meet difficulty to define the multiple costs on the same cost scale. In this paper, we address the problem by building a cost-sensitive decision tree by involving two kinds of cost scales, that minimizes the one kind of cost and control the other in a given specific budget. Our work will be useful for many diagnostic tasks involving target cost minimization and resource consumption for obtaining missing information. © Springer-Verlag Berlin Heidelberg 2004.

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http://hdl.handle.net/10453/1774