Presentation	2011/6/16 Development of rule emsumble method for survival data TOSIO SHIMOKAWA, MITSUHIRO TSUJI,
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Abstract(in English)	One of the important themes in survival analysis is to explore prognoses factors that influence survival time. Recently, the tree-structured method has been applied to evaluate covariates; however, it is well known that this method has provides poor prediction model. This problem could be improved by modeling many trees in a linear combination, namely, ensemble learning. The ensemble learning method is actively studied in machine learning and statistics. In this presentation, we extended the rule ensumble method to analyze survival data, namely survival rule fit method (SRF method). SRF model is constructed by Cox proportional hazard model, and weight (regression) parameters for each rule (base learner) are estimated by lasso.
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Paper #	Vol.2011-BIO-25 No.8
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Committee	NC
Conference Date	2011/6/16(1days)
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Registration To	Neurocomputing (NC)
Language	JPN
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Title (in English)	Development of rule emsumble method for survival data
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1st Author's Name	TOSIO SHIMOKAWA
1st Author's Affiliation	Graduate School of Medicine and Engineering, University of Yamanashi()
2nd Author's Name	MITSUHIRO TSUJI
2nd Author's Affiliation	Faculty of Informatics, Kansai University
Date	2011/6/16
Paper #	Vol.2011-BIO-25 No.8
Volume (vol)	vol.111
Number (no)	96
Page	pp.pp.-
#Pages	8
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