Optimizing the proportion of prototypes generation in nearest neighbor classification

Jui Le Chen, Chu-Sing Yang

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

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].

原文English
主出版物標題Proceedings - International Conference on Machine Learning and Cybernetics
發行者IEEE Computer Society
頁面1695-1699
頁數5
ISBN(電子)9781479902576
DOIs
出版狀態Published - 2013 一月 1
事件12th International Conference on Machine Learning and Cybernetics, ICMLC 2013 - Tianjin, China
持續時間: 2013 七月 142013 七月 17

出版系列

名字Proceedings - International Conference on Machine Learning and Cybernetics
4
ISSN(列印)2160-133X
ISSN(電子)2160-1348

Other

Other12th International Conference on Machine Learning and Cybernetics, ICMLC 2013
國家China
城市Tianjin
期間13-07-1413-07-17

All Science Journal Classification (ASJC) codes

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

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  • 引用此

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