A Strategy for Attributes Selection in Cost-Sensitive Decision Trees Induction

Zhang, S; Liu, L; Zhu, X; Shan, C

A Strategy for Attributes Selection in Cost-Sensitive Decision Trees Induction

Zhang, S Liu, L Zhu, X Shan, C

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Publisher:: IEEE Computer Society
Publication Type:: Conference Proceeding
Citation:: CIT Workshops 2008, 2008, pp. 8 - 13
Issue Date:: 2008-01

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Field	Value	Language
dc.contributor.author	Zhang, S	en_US
dc.contributor.author	Liu, L	en_US
dc.contributor.author	Zhu, X	en_US
dc.contributor.author	Shan, C	en_US
dc.contributor.editor	He, X	en_US
dc.contributor.editor	Wu, Q	en_US
dc.contributor.editor	Nguyen, QV	en_US
dc.contributor.editor	Jia, W	en_US
dc.date	2008-07-08	en_US
dc.date.issued	2008-01	en_US
dc.identifier.citation	CIT Workshops 2008, 2008, pp. 8 - 13	en_US
dc.identifier.isbn	978-0-7695-3339-1	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10876
dc.description.abstract	Decision tree learning is one of the most widely used and practical methods for inductive inference. A fundamental issue in decision tree inductive learning is the attribute selection measure at each non-terminal node of the tree. However, existing literatures have not taken both classification ability and cost-sensitive into account well. In this paper, we present a new strategy for attributes selection, which is a trade-off method between attributesâ information and cost-sensitive learning including misclassification costs and test costs with different units, for selecting splitting attributes in cost-sensitive decision trees induction. The experimental results show our method outperform than the existing methods, such as, information gain method, total costs methods, in terms of the decrease of misclassification costs with different missing rate and various costs in UCI datasets.	en_US
dc.publisher	IEEE Computer Society	en_US
dc.relation.ispartof	CIT Workshops 2008	en_US
dc.relation.ispartof	IEEE International Conference on Computer and Information Technology	en_US
dc.relation.isbasedon	10.1109/CIT.2008.Workshops.51	en_US
dc.rights	© 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	A Strategy for Attributes Selection in Cost-Sensitive Decision Trees Induction	en_US
dc.type	Conference Proceeding
utslib.location	Los Alamitos, USA	en_US
utslib.location.activity	Sydney, Australia	en_US
utslib.for	080604 Database Management	en_US
dc.location.activity	Sydney, Australia	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
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	Los Alamitos, USA	en_US
pubs.start-date	2008-07-08	en_US

Abstract:

Decision tree learning is one of the most widely used and practical methods for inductive inference. A fundamental issue in decision tree inductive learning is the attribute selection measure at each non-terminal node of the tree. However, existing literatures have not taken both classification ability and cost-sensitive into account well. In this paper, we present a new strategy for attributes selection, which is a trade-off method between attributesâ information and cost-sensitive learning including misclassification costs and test costs with different units, for selecting splitting attributes in cost-sensitive decision trees induction. The experimental results show our method outperform than the existing methods, such as, information gain method, total costs methods, in terms of the decrease of misclassification costs with different missing rate and various costs in UCI datasets.

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http://hdl.handle.net/10453/10876