Abstract
As practical pattern classification tasks are often very-large scale and serious imbalance such as patent classification, using traditional pattern classification techniques in a plain way to deal with these tasks has shown inefficient and ineffective. In this paper, a supervised clustering algorithm based on min-max modular network with Gaussian-zero-crossing function is adopted to prune training samples in order to reduce training time and improve generalization accuracy. The effectiveness of the proposed training sample pruning method is verified on a group of real patent classification tasks by using support vector machines and nearest neighbor algorithm.
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
Bansal, N., Blum, A., Chawla, S.: Correlation clustering. Machine Learning 56(1), 89–113 (2004)
Eick, C., Zeidat, N., Zhao, Z.: Supervised clustering–algorithms and benefits. In: International Conference on Tools with Artificial Intelligence, pp. 774–776 (2004)
Fall, C.J., Benzineb, K.: Literature survey: Issues to be considered in the automatic classification of patents. World Intellectual Property Organization 29 (2002)
Fujii, A., Iwayama, M., Kando, N.: Overview of patent retrieval task at NTCIR-5. In: Proceedings of the 5th TCIR Workshop Meeting, pp. 269–277 (2005)
Kohonen, T.: Improved versions of learning vector quantization. In: International Joint Conference on Neural Networks, pp. 545–550 (1990)
Li, J., Lu, B.: A new supervised clustering algorithm based on min-max modular network with Gaussian-zero-crossing functions. In: International Joint Conference on Neural Networks, pp. 786–793.
Li, X., Ye, N.: Grid-and dummy-cluster-based learning of normal and intrusive clusters for computer intrusion detection. Quality and Reliability Engineering International 18(3), 231–242 (2002)
Li, X., Ye, N.: A supervised clustering algorithm for computer intrusion detection. Knowledge and Information Systems 8(4), 498–509 (2005)
Lu, B., Ichikawa, M.: A Gaussian zero-crossing discriminant function for min-max modular neural networks. In: Knowledge-based Intelligent Information Engineering Systems and Allied Technologies, pp. 298–302 (2001)
Lu, B., Ito, M.: Task decomposition and module combination based on class relations: A modular neural network for pattern classification. IEEE Transactions on Neural Networks 10(5), 1244–1256 (1999)
Lu, B., Li, J.: A min-max modular network with Gaussian-zero-crossing function. Trends in Neural Computation, 285–313 (2007)
Sebastiani, F.: Machine learning in automated text categorization. ACM computing surveys 34(1), 1–47 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Huang, M., Zhao, H., Lu, BL. (2010). Pruning Training Samples Using a Supervised Clustering Algorithm. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13318-3_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-13318-3_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13317-6
Online ISBN: 978-3-642-13318-3
eBook Packages: Computer ScienceComputer Science (R0)