Optimizing the proportion of prototypes generation in nearest neighbor classification

Jui Le Chen, Chu-Sing Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Most of the methods for prototype generation that gives a suggestion for the proportional to classes label is equal to the average, but does not completely arrive at ideal accuracy. In this paper, we modify the encoded form of the individual to combine with the proportion for each class label as the extra attributes in each individual solution, besides the use of the DE algorithm with the Pittsburgh's encoding method that include the attributes of all of the prototypes and get the perfect accuracy, and then to raise up the rate of prediction accuracy. The second contribution of this paper is find out that for each numeric attribute value should be normalized to transform to the range [¿1, 1] that get the better accuracy result than the range [0, 1].

Original languageEnglish
Title of host publicationProceedings - International Conference on Machine Learning and Cybernetics
PublisherIEEE Computer Society
Pages1695-1699
Number of pages5
ISBN (Electronic)9781479902576
DOIs
Publication statusPublished - 2013 Jan 1
Event12th International Conference on Machine Learning and Cybernetics, ICMLC 2013 - Tianjin, China
Duration: 2013 Jul 142013 Jul 17

Publication series

NameProceedings - International Conference on Machine Learning and Cybernetics
Volume4
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348

Other

Other12th International Conference on Machine Learning and Cybernetics, ICMLC 2013
CountryChina
CityTianjin
Period13-07-1413-07-17

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Human-Computer Interaction

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  • Cite this

    Chen, J. L., & Yang, C-S. (2013). Optimizing the proportion of prototypes generation in nearest neighbor classification. In Proceedings - International Conference on Machine Learning and Cybernetics (pp. 1695-1699). [6890871] (Proceedings - International Conference on Machine Learning and Cybernetics; Vol. 4). IEEE Computer Society. https://doi.org/10.1109/ICMLC.2013.6890871