Cost sensitive classification in data mining

Qin, Z; Zhang, C; Wang, T; Zhang, S

Cost sensitive classification in data mining

Qin, Z Zhang, C

Wang, T Zhang, S

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, 6440 LNAI (PART 1), pp. 1 - 11
Issue Date:: 2010-12-21

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Field	Value	Language
dc.contributor.author	Qin, Z	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.contributor.author	Wang, T	en_US
dc.contributor.author	Zhang, S	en_US
dc.date.issued	2010-12-21	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, 6440 LNAI (PART 1), pp. 1 - 11	en_US
dc.identifier.isbn	3642173152	en_US
dc.identifier.isbn	9783642173158	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/16286
dc.description.abstract	Cost-sensitive classification is one of mainstream research topics in data mining and machine learning that induces models from data with unbalance class distributions and impacts by quantifying and tackling the unbalance. Rooted in diagnosis data analysis applications, there are great many techniques developed for cost-sensitive learning. They are mainly focused on minimizing the total cost of misclassification costs, test costs, or other types of cost, or a combination among these costs. This paper introduces the up-to-date prevailing cost-sensitive learning methods and presents some research topics by outlining our two new results: lazy-learning and semi-learning strategies for cost-sensitive classifiers. © 2010 Springer-Verlag.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-17316-5_1	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Cost sensitive classification in data mining	en_US
dc.type	Conference Proceeding
utslib.citation.volume	PART 1	en_US
utslib.citation.volume	6440 LNAI	en_US
utslib.for	080604 Database Management	en_US
dc.location.activity	Chongqing, China	en_US
dc.location.activity	ISI:000265273900009
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.issue	PART 1	en_US
pubs.publication-status	Published	en_US
pubs.volume	6440 LNAI	en_US

Abstract:

Cost-sensitive classification is one of mainstream research topics in data mining and machine learning that induces models from data with unbalance class distributions and impacts by quantifying and tackling the unbalance. Rooted in diagnosis data analysis applications, there are great many techniques developed for cost-sensitive learning. They are mainly focused on minimizing the total cost of misclassification costs, test costs, or other types of cost, or a combination among these costs. This paper introduces the up-to-date prevailing cost-sensitive learning methods and presents some research topics by outlining our two new results: lazy-learning and semi-learning strategies for cost-sensitive classifiers. © 2010 Springer-Verlag.

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http://hdl.handle.net/10453/16286