A training sample sequence planning method for pattern recognition problems

Chen Wen Yen, Chieh Neng Young, Mark L. Nagurka

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish
Pages (from-to)575-581
Number of pages7
Issue number4
StatePublished - 1 Apr 2005


  • Classification
  • Decision boundary
  • Nearest-neighbor rule
  • Neural networks
  • Training set editing

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


Dive into the research topics of 'A training sample sequence planning method for pattern recognition problems'. Together they form a unique fingerprint.

Cite this