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How Many Neighbors To Consider in Pattern Pre-selection for Support Vector Classifiers?

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84217

Shin,  H
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Shin, H. (2003). How Many Neighbors To Consider in Pattern Pre-selection for Support Vector Classifiers? In IJCNN 2003 (pp. 565-570).


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-DC1E-2
Abstract
Training support vector classifiers (SVC) requires large memory and long cpu time when the pattern set is large. To alleviate the computational burden in SVC training, we previously proposed a preprocessing algorithm which selects only the patterns in the overlap region around the decision boundary, based on neighborhood properties [8], [9], [10]. The k-nearest neighbors’ class label entropy for each pattern was used to estimate the pattern’s proximity to the decision boundary. The value of parameter k is critical, yet has been determined by a rather ad-hoc fashion. We propose in this paper a systematic procedure to determine k and show its effectiveness through experiments.