Abstract
To solve the classification problem in data mining, this paper proposes double SMO algorithm based on attributes reduction. Firstly attributes reduction deletes irrelevant attributes (or dimensions) to reduce data amount, consequently the total calculation is reduced, the training speed is fastened and Classification mode is easy to understand. Secondly applying SMO algorithm on the sampling dataset to get the approximate separating hyperplane, and then we obtain all the support vectors of original dataset. Finally again use SMO algorithm on the support vectors to get the final separating hyperplane. It is shown in the experiments that the algorithm reduces the memory space, effectively avoids the noise points’ effect on the final separating hyperplane and the precision of the algorithm is better than Decision Tree, Bayesian and Neural Network.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Quinlan, J.R.: Induction of Decision Trees. Machine Learning 1(1), 81–106 (1986)
Gomgde, G., Hui, W., David, B., et al.: KNN Model-based Approach in Classification. In: International Conference on Ontologism, Database and Application of Semantics, Sicily, Italy, pp. 156–160 (2003)
Domingos, P., Pazzani, M.: Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier. In: Proc. 13th Conf. Machine Learning, Sicily, Italy, pp. 105–112 (1996)
Lacher, R.C.: Back-Propagation Learning in Expert Network. IEEE Transaction on Neural Networks 1(3), 62–72 (2002)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Osuna, E., Freund, R., Girosi, F.: Training Support Vector Machines: An Application to Face Detection. In: The IEEE International Conference on Computer Vision and Pattern Recognition, pp. 130–136. IEEE Press, New York (1997)
Joachims, T.: Making Large-scale SVM Llearning Practical. MIT Press, Cambridge (1998)
Platt, J.C.: Fast Training of SVM Using Sequential Minimal Optimization. Support Vector Learning 5(4), 81–106 (1998)
Solla, S., Leen, T., Muller, R.: Advances in Neural Information Processing Systems. IEEE Transactions on Neural Networks 9(3), 932–937 (2000)
Mangasarian, O.L.: Successive over Relaxation for Support Vector Machines. IEEE Transcations on Neural Networks 10(12), 1032–1037 (2003)
Kiranoudis, C.T., Koumiotis, S.P., Christolis, M., et al.: An Operational Centre for Managing Major Chemical Industrial Accidents. Journal of Hazardous Materials 8(8), 141–161 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, C., Hong, L., Song, H., Chen, X., Hou, T. (2009). Study of Double SMO Algorithm Based on Attributes Reduction. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_42
Download citation
DOI: https://doi.org/10.1007/978-3-642-01510-6_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01509-0
Online ISBN: 978-3-642-01510-6
eBook Packages: Computer ScienceComputer Science (R0)