Local bayesian based rejection method for HSC ensemble

Authors:
Qing He;Wenjuan Luo;Fuzhen Zhuang;Zhongzhi Shi
Affiliations:
The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;The Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Venue:
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Year:
2010

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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.