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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Zhao, XR., He, Q., Shi, ZZ. (2006). HyperSurface Classifiers Ensemble for High Dimensional Data Sets. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_193
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DOI: https://doi.org/10.1007/11759966_193
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34439-1
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