HyperSurface classifiers ensemble for high dimensional data sets

Authors:
Xiu-Rong Zhao;Qing He;Zhong-Zhi Shi
Affiliations:
The Key Laboratory of Intelligent Information Processing, Department of Intelligence Software, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;The Key Laboratory of Intelligent Information Processing, Department of Intelligence Software, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;The Key Laboratory of Intelligent Information Processing, Department of Intelligence Software, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

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Abstract

Based on Jordan Curve Theorem, a universal classification method called HyperSurface Classifier (HSC) has recently been proposed. Experimental results show that in three-dimensional space, this method works fairly well in both accuracy and efficiency even for large size data up to 107. However, what we really need is an algorithm that can deal with data not only of massive size but also of high dimensionality. In this paper, an approach based on the idea of classifiers ensemble by dimension dividing without dimension reduction for high dimensional data is proposed. The most important difference between HSC ensemble and the traditional ensemble is that the sub-datasets are obtained by dividing the features rather than by dividing the sample set. Experimental results show that this method has a preferable performance on high dimensional datasets.