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Prototype optimization for nearest-neighbor classification

✍ Scribed by Y.S. Huang; C.C. Chiang; J.W. Shieh; E. Grimson

Publisher
Elsevier Science
Year
2002
Tongue
English
Weight
238 KB
Volume
35
Category
Article
ISSN
0031-3203

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✦ Synopsis

A novel neuralnet-based method of constructing optimized prototypes for nearest-neighbor classiÿers is proposed. Based on an e ective classiÿcation oriented error function containing class classiÿcation and class separation components, the corresponding prototype and feature weight update rules are derived. The proposed method consists of several distinguished properties. First, not only prototypes but also feature weights are constructed during the optimization process. Second, several instead of one prototype not belonging to the genuine class of input sample x are updated when x is classiÿed incorrectly. Third, it intrinsically distinguishes di erent learning contribution from training samples, which enables a large amount of learning from constructive samples, and limited learning from outliers. Experiments have shown the superiority of this method compared with LVQ2 and other previous works.

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