Liu, ChengChengLiuLiang, YongYongLiangLuan, Xin-ZeXin-ZeLuanProf. LEUNG Kwong SakChan, Tak-MingTak-MingChanXu, Zong-BenZong-BenXuZhang, HaiHaiZhang2023-03-172023-03-172012Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7332 LNCS(PART 2), pp. 11-17978-364231019-516113349http://hdl.handle.net/20.500.11861/7515In this paper, we investigate to use theL1/2 regularization method for variable selection based on the Cox's proportional hazards model. The L1/2 regularization method isa reweighed iterative algorithm with the adaptively weighted L1 penalty on regression coefficients. The algorithm of the L1/2 regularization method can be easily obtained by a series of L1 penalties. Simulation results based on standard artificial data show that the L1/2 regularization method can be more accurate for variable selection than Lasso and adaptive Lasso methods. The results from Primary Biliary Cirrhosis (PBC) dataset indicate the L 1/2 regularization method performs competitively. © 2012 Springer-Verlag.enLassoL 1/2 RegularizationVariable SelectionCox ModelConference Paper10.1007/978-3-642-31020-1_2