A training sample sequence planning method for pattern recognition problems

Authors:
Chen-Wen Yen;Chieh-Neng Young;Mark L. Nagurka
Affiliations:
Department of Mechanical Engineering, National Sun-Yat Sen University, Kaohsiung, Taiwan 80424, China;Department of Mechanical Engineering, National Sun-Yat Sen University, Kaohsiung, Taiwan 80424, China;Department of Mechanical and Industrial Engineering, Marquette University, P.O. Box 1881, Milwaukee, WI 53201-1881, USA
Venue:
Automatica (Journal of IFAC)
Year:
2005

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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.