A Training Sample Sequence Planning Method for Pattern Recognition Problems

Authors

Chen-Wen Yen, National Sun Yat-Sen UniversityFollow
Chieh-Neng Young, National Sun Yat-Sen UniversityFollow
Mark L. Nagurka, Marquette UniversityFollow

Document Type

Article

Language

eng

Publication Date

4-2005

Publisher

Elsevier

Source Publication

Automatica

Source ISSN

0005-1098

Abstract

In solving pattern recognition problems, many classification methods, such as the nearest-neighbor (NN) rule, need to determine prototypes from a training set. To improve the performance of these classifiers in finding an efficient set of prototypes, this paper introduces a training sample sequence planning method. In particular, by estimating the relative nearness of the training samples to the decision boundary, the approach proposed here incrementally increases the number of prototypes until the desired classification accuracy has been reached. This approach has been tested with a NN classification method and a neural network training approach. Studies based on both artificial and real data demonstrate that higher classification accuracy can be achieved with fewer prototypes.

Comments

Accepted version. Automatica, Vol. 41, No. 4 (April 2005): 575-581. DOI. © 2004 Elsevier Ltd. Used with permission.

Recommended Citation

Yen, Chen-Wen; Young, Chieh-Neng; and Nagurka, Mark L., "A Training Sample Sequence Planning Method for Pattern Recognition Problems" (2005). Mechanical Engineering Faculty Research and Publications. 152.
https://epublications.marquette.edu/mechengin_fac/152