Abstract
This paper presents a training set decomposition of price-sensitive support vector machines for classification Firstly, decomposition of the training sample set, each subset of training a support vector machine can output a posteriori probability, get through the training of support vector machine on the training sample posterior probability using the learning process and the cost matrix, the true class label of the sample, in order to achieve the reconstruction of the sample, contains a sample misclassification cost information, cost-sensitive support vector machines, making the classification of unbalanced data sets, so that the smallest misclassification cost.
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
Han, H., Wang, W.-Y., Mao, B.-H.: Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 878–887. Springer, Heidelberg (2005)
Tang, S., Chen, S.P.: The generation mechanism of synthetic minority class examples. In: International Conference on Information Technology and Applications in Biomedicine, pp. 444–447 (2008)
Wang, J.J., Xu, M.T., Wang, H., Zhang, J.W.: Classification of imbalanced data by using the SMOTE algorithm and locally linear embedding. In: The 8th International Conference on Signal Processing (2007)
Zhou, Z.H., Liu, X.Y.: Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Trans. Knowl. Data Eng. 18(1), 63–77 (2006)
Akbani, R., Kwek, S.S., Japkowicz, N.: Applying Support Vector Machines to Imbalanced Datasets. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 39–50. Springer, Heidelberg (2004)
Weiss, G.M.: Mining with rarity: a unifying framework. ACM SIGKDD Explorations 6(1), 7–19 (2004)
Kubat, M., Matwin, S.: Addressing the curse of imbalanced datasets.One-sided Sampling. In: Proceedings of the Fourteenth International Conference on Machine Learing, pp. 178–186. Tennessee, Nashville (1997)
Weiss, G.M.: Mining with rarity: a unifying framework. ACM SIGKDD Explorations 6(1), 7–19 (2004)
Drown, D.J., Khoshgoftaar, T.M., Narayanan, R.: Using evolutionary sampling to mine imbalanced data. In: The 6th International Conference on Machine Learning and Applications, pp. 363–368. IEEE Computer Society, Washington (2007)
Chawla, N.V., Cieslak, D.A., Hall, L.O., et al.: Automatically Countering Imbalance and Empirical Relationship to Cost. Data Mining and Knowledge Discovery 17(2), 225–252 (2008)
Garcia, S., Herrera, F.: Evolutionary Under-Sampling for Classification with Imbalanced DataSets: Proposal sand Taxonomy. Evolutionary Computation 17(3), 275–306 (2008)
Han, H., Wang, W.-Y., Mao, B.-H.: Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 878–887. Springer, Heidelberg (2005)
Hulse, J.V., Khoshgoftaar, T.M., Napolitano, A.: Experimental Perspectives on Learning from Imbalanced Data. In: Proceedings of the 24th International Conference on Machine Learning (ICML 2007), pp. 935–942. ACM, New York (2007)
Wu, H., Xu, J.B., Zhang, S.F., Wen, H.: GPU Accelerated Dissipative Particle Dynamics with Parallel Cell-list Updating. IEIT Journal of Adaptive & Dynamic Computing 2011(2), 26–32 (2011) DOI=10.5813/www.ieit-web.org/IJADC/2011.2.4
Zhou, J.J.: The Parallelization Design of Reservoir Numerical Simulator. IEIT Journal of Adaptive & Dynamic Computing 2011(2), 33–37 (2011), DOI=10.5813/www.ieit-web.org/IJADC/2011.2.5
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guilin, X. (2013). Price Sensitive Support Vector Machine Based on Data Set Decomposition. In: Du, Z. (eds) Proceedings of the 2012 International Conference of Modern Computer Science and Applications. Advances in Intelligent Systems and Computing, vol 191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33030-8_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-33030-8_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33029-2
Online ISBN: 978-3-642-33030-8
eBook Packages: EngineeringEngineering (R0)