A prototype generation with same class label proportion method for nearest neighborhood classification

Jui Le Chen, Ko Wei Huang, Pang Wei Tsai, Chu Sing Yang

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

Abstract

The KNN algorithm has a significant effect on classification prediction in Data Mining. In order to solve the drawbacks for KNN algorithm to reduce the costs of the calculation and increase the accuracy, this paper proposed a prototype generation method with same class label proportion for classification to ensure that each class has at least a prototype to be represented. We compare the average success rate of GA, PSO, DE and proposed method SPDE. The experimental results show that the SPDE has more opportunity to do better than others in those problems.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages96-97
Number of pages2
ISBN (Electronic)9781479987443
DOIs
Publication statusPublished - 2015 Aug 20
Event2nd IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2015 - Taipei, Taiwan
Duration: 2015 Jun 62015 Jun 8

Publication series

Name2015 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2015

Other

Other2nd IEEE International Conference on Consumer Electronics - Taiwan, ICCE-TW 2015
Country/TerritoryTaiwan
CityTaipei
Period15-06-0615-06-08

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Instrumentation
  • Media Technology

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