Abstract
Based on Jordan Curve Theorem, a universal classification method, called Hyper Surface Classifier (HSC) was proposed in 2002. Experiments showed the efficiency and effectiveness of this algorithm. Afterwards, an ensemble manner for HSC(HSC Ensemble), which generates sub classifiers with every 3 dimensions of data, has been proposed to deal with high dimensional datasets. However, as a kind of covering algorithm, HSC Ensemble also suffers from rejection which is a common problem in covering algorithms. In this paper, we propose a local bayesian based rejection method(LBBR) to deal with the rejection problem in HSC Ensemble. Experimental results show that this method can significantly reduce the rejection rate of HSC Ensemble as well as enlarge the coverage of HSC. As a result, even for datasets of high rejection rate more than 80%, this method can still achieve good performance.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Furnkranz, J.: ROC n Rule Learning Towards a Better Understanding of Covering Algorithms. Machine Learning 58, 39–77 (2005)
Zhang, L., Zhang, B.: A Geometrical Representation of McCullochCPitts Neural Model and Its Applications. IEEE Transactions on Neural Networks 10(4) (1999)
Wu, T., Zhang, L., Yan-Ping, Z.: Kernel Covering Algorithm for Machine Learning. Chinese Journal of Computers 28(8) (2005)
He, Q., Shi, Z.-Z., Ren, L.-A., Lee, E.S.: A Novel Classification Method Based on Hyper Surface. International Journal of Mathematical and Computer Modeling, 395–407 (2003)
Pan, S.J., Yang, Q.: A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering (October 12, 2009)
Landgrebe, T.C.W., Tax, D.M.J., Paclk, P., Duin, R.P.W.: The interaction between classification and reject performance for distance-based reject-option classifiers. Pattern Recognition Letters 27(8), 908–917 (2006)
Dubuisson, B., Masson, M.: A statistical decision rule with incomplete knowledge about classes. Pattern Recognition 26(1), 155–165 (1993)
Landgrebe, T., Tax, D., Paclk, P., Duin, R., Andrew, C.: A combining strategy for ill-defined problems. In: Fifteenth Ann. Sympos. of the Pattern Recognition Association of South Africa, pp. 57–62 (2004)
Zhang, L., Wu, T., Zhou, Y., Zhang, Y.P.: Probabilistic Model for Covering Algorithm. Journal of Software 18(11), 2691–2699 (2007)
He, Q., Zhao, X., Shi, Z.: Classification based on dimension transposition for high dimension dataInternational Journal Soft Computing-A Fusion of Foundations. Methodologies and Applications, 329–334 (2006)
Zhao, X.R., He, Q., Shi, Z.Z.: HyperSurface Classifiers Ensemble for High Dimensional Data sets. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3971, pp. 1299–1304. Springer, Heidelberg (2006)
He, Q., Zhao, X.-R., Shi, Z.-Z.: Minimal consistent subset for hyper surface classification method. International Journal of Pattern Recognition and Artificial Iintelligence 22(1) (2008)
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
He, Q., Luo, W., Zhuang, F., Shi, Z. (2010). Local Bayesian Based Rejection Method for HSC Ensemble. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13278-0_52
Download citation
DOI: https://doi.org/10.1007/978-3-642-13278-0_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13277-3
Online ISBN: 978-3-642-13278-0
eBook Packages: Computer ScienceComputer Science (R0)