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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Support Vector Based Prototype Selection Method for Nearest Neighbor Rules
verfasst von : Yuangui Li, Zhonghui Hu, Yunze Cai, Weidong Zhang
Erschienen in: Advances in Natural Computation
Verlag: Springer Berlin Heidelberg
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The Support vector machines derive the class decision hyper planes from a few, selected prototypes, the support vectors (SVs) according to the principle of structure risk minimization, so they have good generalization ability. We proposed a new prototype selection method based on support vectors for nearest neighbor rules. It selects prototypes only from support vectors. During classification, for unknown example, it can be classified into the same class as the nearest neighbor in feature space among all the prototypes. Computational results show that our method can obtain higher reduction rate and accuracy than popular condensing or editing instance reduction method.