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Iterative L1/2 Regularization Algorithm for Variable Selection in the Cox Proportional Hazards Model

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Advances in Swarm Intelligence (ICSI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7332))

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Abstract

In this paper, we investigate to use theL 1/2 regularization method for variable selection based on the Cox’s proportional hazards model. The L 1/2 regularization method isa reweighed iterative algorithm with the adaptively weighted L 1 penalty on regression coefficients. The algorithm of theL 1/2 regularization method can be easily obtained by a series of L 1 penalties. Simulation results based on standard artificial data show that theL 1/2 regularization method can be more accurate for variable selection than Lasso and adaptive Lasso methods. The results from Primary Biliary Cirrhosis (PBC) dataset indicate theL 1/2 regularization method performs competitively.

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Author information

Authors and Affiliations

  1. Macau University of Science and Technology, China

    Cheng Liu, Yong Liang & Xin-Ze Luan

  2. Chinese University of Hong Kong, Hong Kong

    Kwong-Sak Leung & Tak-Ming Chan

  3. Xi’an Jiaotong University, Xi’an, China

    Zong-Ben Xu & Hai Zhang

Authors
  1. Cheng Liu
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  2. Yong Liang
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  3. Xin-Ze Luan
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  4. Kwong-Sak Leung
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  5. Tak-Ming Chan
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  6. Zong-Ben Xu
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  7. Hai Zhang
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Editor information

Editors and Affiliations

  1. Key Laboratory of Machine Perception (MOE), Peking University, Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, 100871, Beijing, China

    Ying Tan

  2. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  3. Shenzhen City Key Laboratory of Embedded System Design, College of Computer Science and Software Engineering, Shenzhen University, 518060, Shenzhen, China

    Zhen Ji

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© 2012 Springer-Verlag Berlin Heidelberg

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Liu, C. et al. (2012). Iterative L1/2 Regularization Algorithm for Variable Selection in the Cox Proportional Hazards Model. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7332. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31020-1_2

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  • DOI: https://doi.org/10.1007/978-3-642-31020-1_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31019-5

  • Online ISBN: 978-3-642-31020-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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