An efficient design of a nearest neighbor classifier for various-scale problems

Heesung Lee, Sungjun Hong, Imran Fareed Nizami, Euntai Kim

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


By appropriate editing of the reference set and judicious selection of features, we can obtain an optimal nearest neighbor (NN) classifier that maximizes the accuracy of classification and saves computational time and memory resources. In this paper, we propose a new method for simultaneous reference set editing and feature selection for a nearest neighbor classifier. The proposed method is based on the genetic algorithm and employs different genetic encoding strategies according to the size of the problem, such that it can be applied to classification problems of various scales. Compared with the conventional methods, the classifier uses some of the considered references and features, not all of them, but demonstrates better classification performance. To demonstrate the performance of the proposed method, we perform experiments on various databases.

Original languageEnglish
Pages (from-to)1020-1027
Number of pages8
JournalPattern Recognition Letters
Issue number9
Publication statusPublished - 2010 Jul 1

Bibliographical note

Funding Information:
This work was supported by Grant No. R01-2006-000-11016-0 from Basic Research Program of the Korea Science & Engineering Foundation .

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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