Abstract
This paper focuses on feature selection in classification. A new version of support vector machine (SVM) named p-norm support vector machine (\(p\in[0,1]\)) is proposed. Different from the standard SVM, the p-norm \((p\in[0,1])\) of the normal vector of the decision plane is used which leads to more sparse solution. Our new model can not only select less features but also improve the classification accuracy by adjusting the parameter p. The numerical experiments results show that our p-norm SVM is more effective than some usual methods in feature selection.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46:389–422
Tan JY, Whang H, Deng NY (2010) Recursive group elimination based on support vector machine. J Syst Sci Inf 8(1):1–14
Bradley PS, Mangasarian OL, Street WN (1998) Feature selection via mathematical programming. INFORMS J Comput. doi:10.1287/ijoc.10.2.209
Bradley PS, Mangasarian OL (1998) Feature selection via concave minimization and support vector machines. In: Proceedings of 13th ICML, pp 82–90
Mangasarian OL, Wild EW (2007) Feature selection for nonlinear kernel support vector machines. In: IEEE seventh international conference on data mining (ICDM’07)
Ideka K, Murata N (2005) Learning properties of support vector machine with p-norm. In: IEEE 47th midwest symposium on circuits and systems, pp 69–72
Zhang HH, Ahn JY, Lin XD, Park CW (2006) selection suing suport vector machines with nonconvex penalty. Bioinformatics 22:88–96
Wang L, Zhu J, Zou H (2008) Hybrid huberized support vector machines for microarray classification and gene selection. Bioinformatics 23:2507–2517
Chen XJ, Xu FM, Ye YY (2009) Lower bound theory of nonzero entries in solutions of l 2-l p minimization
Bruckstein AM, Donoho DL, Elad M (2009) From sparse sulutions of systems of equations to sparse modeling of signals and images. SIAM Rev 51:34–81
Fan J, Li R (2001) Varible selection via nonconcave penalized likelihood and its oracle properties. J Am Stat Assoc 96:1348–1360
Xu Z, Zhang H, Wang Y, Chang X (2009) \(L_{{\frac{1}{2}}} \) regularizer. Sci China Ser F InfSci 52:1–9
Chen WJ, Tian YJ (2010) L p -norm proximal support vector machine and its applications. Proc Comput Sci ICCS 1(1):2417–2423
Tian YJ, Yu J, Chen WJ (2010) l p -norm support vector machine with CCCP. In: Proceedings of the 7th FSKD, pp 1560–1564
Tan JY, Zhang CH, Deng NY (2010) Cancer related gene identification via p-norm support vector machine. In: The 4th international conference on computational systems biology, pp 101–108
Blake CL, Merz CJ (1998) UCI repository of machine learning database. University of California. http://www.ics.uci.edu/mlearn/MLRepository.html
Singh D, Febbo P, Ross K, Jackson D, Manola J, Ladd C, Tamayo P, Renshaw A, D’Amico A, Richie J, Lander E, Loda M, Kantoff P, Golub T, Sellers W (2002) Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1:203–209
Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286:531–537
Acknowledgments
This work is supported by Chinese Universities Scientific Fund (No. 2011JS039) and the National Natural Science Foundation of China (No. 10971223).
Author information
Authors and Affiliations
Corresponding authors
Rights and permissions
About this article
Cite this article
Tan, J., Zhang, Z., Zhen, L. et al. Adaptive feature selection via a new version of support vector machine. Neural Comput & Applic 23, 937–945 (2013). https://doi.org/10.1007/s00521-012-1018-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-012-1018-y