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Soft Computing

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01.01.2014 | Methodologies and Application

A novel L_1/2 regularization shooting method for Cox’s proportional hazards model

verfasst von: Xin-Ze Luan, Yong Liang, Cheng Liu, Kwong-Sak Leung, Tak-Ming Chan, Zong-Ben Xu, Hai Zhang

Erschienen in: Soft Computing | Ausgabe 1/2014

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Abstract

Nowadays, a series of methods are based on a L ₁ penalty to solve the variable selection problem for a Cox’s proportional hazards model. In 2010, Xu et al. have proposed a L _1/2 regularization and proved that the L _1/2 penalty is sparser than the L ₁ penalty in linear regression models. In this paper, we propose a novel shooting method for the L _1/2 regularization and apply it on the Cox model for variable selection. The experimental results based on comprehensive simulation studies, real Primary Biliary Cirrhosis and diffuse large B cell lymphoma datasets show that the L _1/2 regularization shooting method performs competitively.

Vorheriger Artikel Systolic neighborhood search on graphics processing units

Nächster Artikel Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system

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Titel: A novel L1/2 regularization shooting method for Cox’s proportional hazards model
verfasst von: Xin-Ze Luan
Yong Liang
Cheng Liu
Kwong-Sak Leung
Tak-Ming Chan
Zong-Ben Xu
Hai Zhang
Publikationsdatum: 01.01.2014
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 1/2014
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-013-1042-6

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