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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© 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
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