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.
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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
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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
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