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Book Chapter: Efficient support vector machine method for survival prediction with SEER data

TitleEfficient support vector machine method for survival prediction with SEER data
Authors
KeywordsSupport vector machines.
Issue Date2010
PublisherSpringer
Citation
Efficient support vector machine method for survival prediction with SEER data. In Arabnia, HR (Ed.), Advances in computational biology, p. 11-18. New York, NY: Springer, 2010 How to Cite?
AbstractSupport vector machine (SVM) is a popular method for classification, but there are few methods that utilize SVM for survival analysis in the literature because of the computational complexity. In this paper, we develop a novel L 1 penalized SVM method for mining right-censored survival data ( L 1 SVMSURV). Our proposed method can simultaneously identify survival-associated prognostic factors and predict survival outcomes. It is easy to understand and efficient to use especially when applied to large datasets. Our method has been examined through both simulation and real data, and its performance is very good with limited experiments.
Persistent Identifierhttp://hdl.handle.net/10722/141498
ISBN
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLiu, ZQen_HK
dc.contributor.authorChen, DCen_HK
dc.contributor.authorTian, Gen_HK
dc.contributor.authorTang, MLen_HK
dc.contributor.authorTan, Men_HK
dc.contributor.authorSheng, Len_HK
dc.date.accessioned2011-09-23T06:39:02Z-
dc.date.available2011-09-23T06:39:02Z-
dc.date.issued2010en_HK
dc.identifier.citationEfficient support vector machine method for survival prediction with SEER data. In Arabnia, HR (Ed.), Advances in computational biology, p. 11-18. New York, NY: Springer, 2010en_HK
dc.identifier.isbn9781441959126-
dc.identifier.urihttp://hdl.handle.net/10722/141498-
dc.description.abstractSupport vector machine (SVM) is a popular method for classification, but there are few methods that utilize SVM for survival analysis in the literature because of the computational complexity. In this paper, we develop a novel L 1 penalized SVM method for mining right-censored survival data ( L 1 SVMSURV). Our proposed method can simultaneously identify survival-associated prognostic factors and predict survival outcomes. It is easy to understand and efficient to use especially when applied to large datasets. Our method has been examined through both simulation and real data, and its performance is very good with limited experiments.en_HK
dc.languageengen_US
dc.publisherSpringeren_US
dc.relation.ispartofAdvances in computational biologyen_HK
dc.subjectSupport vector machines.en_HK
dc.titleEfficient support vector machine method for survival prediction with SEER dataen_HK
dc.typeBook_Chapteren_HK
dc.identifier.emailTian, G: gltian@hku.hken_HK
dc.identifier.authorityTian, G=rp00789en_HK
dc.identifier.doi10.1007/978-1-4419-5913-3_2en_HK
dc.identifier.pmid20865481-
dc.identifier.hkuros195617en_US
dc.identifier.spage11en_HK
dc.identifier.epage18en_HK
dc.identifier.isiWOS:000283006100002-
dc.publisher.placeNew York, NYen_HK
dc.customcontrol.immutableyiu 130628-

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