Abstract
A selective ensemble of support vector machines (SVMs) based on immune clonal algorithm (ICA) is proposed for the case of classification. ICA, a new intelligent computation method simulating the natural immune system, characterized by rapid convergence to global optimal solutions, is employed to select a suitable subset of the trained component SVMs to make up of an ensemble with high generalization performance. The experimental results on some popular datasets from UCI database show that the selective SVMs ensemble outperforms a single SVM and traditional ensemble method that ensemble all the trained component SVMs.
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References
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1999)
Freund, Y.: Boosting a Weak Algorithm by Majority. Information and Computation 121, 256–285 (1995)
Breiman, L.: Bagging Predictors. Machine Learning 24, 123–140 (1996)
Osuna, E., Freund, R., Girosi, F.: Training Support Vector Machines: An Application to Face Detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 130–136 (1997)
Scholkopf, B., Smola, A.J.: Learning with Kernels. MIT Press, Cambridge (2002)
Chapelle, O., Haffner, P., Vapnik, V.: SVMs for Histogram-Based Image Classification. IEEE Trans. on Neural Networks 10, 1055–1065 (1999)
Je, H.M., Kim, D., Bang, S.Y.: Human Face Detection in Digital Video Using SVM Ensemble. Neural Processing Letters 17, 239–252 (2003)
Pang, S.N., Kim, D., Bang, S.Y.: Membership Authentication in the Dynamic Group by Face Classification Using SVM Ensemble. Pattern Recognition Letters 24, 215–225 (2003)
Hansen, L., Salamon, P.: Neural Network Ensembles. IEEE Trans. on Pattern Analysis and Machine Intelligence 12, 993–1001 (1990)
Krogh, A., Vedelsby, J.: Neural Network Ensembles, Cross Validation, and Active Learning. In: Advances in Neural Information Processing Systems, vol. 7, pp. 231–238. MIT Press, Cambridge (1995)
Zhou, Z.H., Wu, J.X., Tang, W.: Ensembling Neural Networks: Many Could Be Better Than All. Artificial Intelligence 137, 239–263 (2002)
Jiao, L.C., Du, H.F.: Development and Prospect of the Artificial Immune System. Acta Electronica Sinica 31, 73–80 (2003)
Zhang, X.R., Shan, T., Jiao, L.C.: SAR Image Classification Based on Immune Clonal Feature Selection. In: Campilho, A.C., Kamel, M.S. (eds.) ICIAR 2004. LNCS, vol. 3212, pp. 504–511. Springer, Heidelberg (2004)
Tumer, K., Ghosh, J.: Error Correlation and Error Reduction in Ensemble Classifiers. Connection Science, Special Issue on Combining Artificial Neural Networks: Ensemble Approaches 8, 385–404 (1996)
Du, H.F., Jiao, L.C., Wang, S.A.: Clonal Operator and Antibody Clone Algorithms. In: Proceedings of the First International Conference on Machine Learning and Cybernetics, Beijing, pp. 506–510 (2002)
Du, H.F., Jiao, L.C., Gong, M.G., Liu, R.C.: Adaptive Dynamic Clone Selection Algorithms. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 768–773. Springer, Heidelberg (2004)
Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases. Department of Information and Computer Science. University of California, Irvine (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
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Zhang, X., Wang, S., Shan, T., Jiao, L. (2005). Selective SVMs Ensemble Driven by Immune Clonal Algorithm. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2005. Lecture Notes in Computer Science, vol 3449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32003-6_33
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DOI: https://doi.org/10.1007/978-3-540-32003-6_33
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