Abstract
A frequent problem in support vector regression is to select appropriate features or parameters. We present an efficient feature selection method for regression problem where optimal kernel weights and model parameters are learned alternatively. Our approach generalizes v support vector regression and can be formulized as quadratic constrained quadratic programming which can be efficiently solved by level method. Moreover, we introduce an elastic-net-type constrain on the kernel weights. It finds the best trade-off sparsity and accuracy. Our algorithm keeps the useful information and discards redundant information; meanwhile it has the similar properties of v parameter. The experimental evaluation of the proposed algorithm on synthetic dataset and stock marketing price forecasting task show that our method can select suitable features for building model and attain competitive performance.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Mehmet, G., Ethem, A.: Multiple Kernel Learning Algorithms. Journal of Machine Learning Research 12, 2211–2268 (2011)
Abbasnejad, M., Ramachandram, D., Mandava, R.: A Survey of The State of The Art in Learning The Kernels. Knowledge and Information Systems 29 (2011)
Qiu, S., Lane, T.: Multiple Kernel Support Vector Regression for siRNA Efficacy Prediction. In: Măndoiu, I., Wang, S.-L., Zelikovsky, A. (eds.) ISBRA 2008. LNCS (LNBI), vol. 4983, pp. 367–378. Springer, Heidelberg (2008)
Yeh, C.Y., Huang, C.W., Lee, S.J.: A Multiple-kernel Support Vector Regression Approach for Stock Market price Forecasting. Expert Syst. Appl. 38, 2177–2186 (2011)
Suard, F., Rakotomamonjy, A., Bensrhair, A.: IEEE: Model Selection in Pedestrian Detection using Multiple Kernel Learning. In: IEEE Intelligent Vehicles Symposium, pp. 824–829 (2007)
Lin, Y.Y., Liu, T.L., Fuh, C.S.: Multiple Kernel Learning for Dimensionality Reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence 33, 1147–1160 (2011)
Vedaldi, A., Gulshan, V., Varma, M., Zisserman, A.: Multiple Kernels for Object Detection. In: Proceedings of the International Conference on Computer Vision (2009)
Rakotomamonjy, A., Bach, F.R., Canu, S., Grandvalet, Y.: SimpleMKL. Journal of Machine Learning Research 9, 2491–2521 (2008)
LIBSVM Dataset: Classification, Regression and Multy-label, http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/
An implementation of Support Vector Machines (SVMs) in C, http://svmlight.joachims.org
Tsang, I.W., Kwok, J.T., Cheung, P.M.: Core Vector Machines: Fast SVM Training on Very Large Data Sets. Journal of Machine Learning Research 6, 363–392 (2005)
Qiu, S.: A Framework for Multiple Kernel Support Vector Regression and Its Applications to siRNA Efficacy Prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics 6, 190–199 (2009)
Gonen, M.: Ethem: Localized Multiple Kernel Regression. In: Proceedings of the 20th IAPR International Conference on Pattern Recognition, Istanbul, Turkey (2010)
Haiqin, Y., Zenglin, X., Jieping, Y., King, I., Lyu, M.R.: Efficient Sparse Generalized Multiple Kernel Learning. IEEE Transactions on Neural Networks 22, 433–446 (2011)
Scholkopf, B., Smola, A.J., Williamson, R.C., Bartlett, P.L.: New support vector algorithms. Neural Computation 12, 1207–1245 (2000)
Vapnik, V.: Statistical Learning Theory. Wiley, NewYork (1998)
Sonnenburg, S., Ratsch, G., Schafer, C., Scholkopf, B.: Large Scale Multiple Kernel Learning. Journal of Machine Learning Research 7, 1531–1565 (2006)
Lanckriet, G.R.G., Cristianini, N., Bartlett, P., El Ghaoui, L., Jordan, M.I.: Learning the Kernel Matrix with Semidefinite Programming. Journal of Machine Learning Research 5, 27–72 (2004)
Kloft, M., Brefeld, U., Laskov, P.: Non-sparse Multiple Kernel Learning. In: NIPS Workshop on Kernel Learning: Automatic Selection of Optimal Kernels (2008)
Kloft, M., Brefeld, U., Sonnenburg, S., Zien, A.: l(p)-Norm Multiple Kernel Learning. Journal of Machine Learning Research 12, 953–997 (2011)
Vapnik, V.N.: Statistical Learning Theory. Wiley-Interscience (1998)
Grandvalet, Y., Canu, S.: Outcomes of the Equivalence of Adaptive Ridge with Least Absolute Shrinkage. Advances in Neural Information Processing Systems, pp. 445–451 (1998)
MOSEK ApS. MOSEK Optimization Software (2010), http://www.mosek.com
Xu, Z., Jin, R., King, I., Lyu, M.: An Extended Level Method for Efficient Multiple Kernel Learning (2009)
Cao, L.J.: Support Vector Machines Experts for Time Series Forecasting. Neurocomputing 51, 321–339 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lin, CZ., Chen, XK. (2012). Multi-Kernel Based Feature Selection for Regression. In: Huang, DS., Ma, J., Jo, KH., Gromiha, M.M. (eds) Intelligent Computing Theories and Applications. ICIC 2012. Lecture Notes in Computer Science(), vol 7390. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31576-3_40
Download citation
DOI: https://doi.org/10.1007/978-3-642-31576-3_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31575-6
Online ISBN: 978-3-642-31576-3
eBook Packages: Computer ScienceComputer Science (R0)