Abstract
Imbalanced data problem has a significant impact on the performance of RVM pattern recognition. Customer identification is an important application domain of pattern recognition which is mapping from samples to different categories by machine learning. In order to solve the problem, the paper proposes a method named up-sampling which overcomes the phenomenon that the machine is more partial to the majority classes while ignoring the sparse and decreases the false judgment about the sparse ones.
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
Tipping, M.E.: The Relevance Vector Machine. In: Advances in Neural Information Processing Systems, vol. 12, pp. 652–658 (2000)
Ling, C.X., Li, C.: Data mining for direct marketing: Problems and solutions, Citeseer (1998)
Pearson, R., Goney, G., Shwaber, J.: Imbalanced clustering for microarray time-series. In: Proceedings of the ICML 2003 Workshop on Learning from Imbalanced Data Sets (2003)
Wilk, S., Sowiski, R., Michaowski, W., et al.: Supporting triage of children with abdominal pain in the emergency room. European Journal of Operational Research 160(3), 696–709 (2005)
Ha, K., Cho, S., Maclachlan, D.: Response models based on bagging neural networks. Journal of Interactive Marketing 19(1), 17–30 (2005)
Ripley, B.D.: Pattern recognition and neural networks. Cambridge Univ. Pr., New York (1996)
Chawla, N.V., Japkowicz, N., Kotcz, A.: Editorial: special issue on learning from imbalanced data sets. ACM SIGKDD Explorations Newsletter 6(1), 1–6 (2004)
Chawla, N.V.: Data mining for imbalanced datasets: An overview. Data Mining and Knowledge Discovery Handbook, 875–886 (2010)
Maloof, M.: Learning when data sets are imbalanced and when costs are unequal and unknown, Citeseer (2003)
Kubat, M., Matwin, S.: Addressing the curse of imbalanced training sets: one-sided selection, Citeseer (1997)
Batista, G.E., Prati, R.C., Monard, M.C.: A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explorations Newsletter 6(1), 20–29 (2004)
Wu, G., Chang, E.Y.: KBA: kernel boundary alignment considering imbalanced data distribution. IEEE Transactions on Knowledge and Data Engineering, 786–795 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, G., Zhang, L., Wang, Gl. (2011). Imbalanced Data Problem of Relevance Vector Machine Customer Identification. In: Zhou, M., Tan, H. (eds) Advances in Computer Science and Education Applications. Communications in Computer and Information Science, vol 202. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22456-0_64
Download citation
DOI: https://doi.org/10.1007/978-3-642-22456-0_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22455-3
Online ISBN: 978-3-642-22456-0
eBook Packages: Computer ScienceComputer Science (R0)