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Conference Paper

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

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

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Citation

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

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 patterns 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.