Using Particle Swarm Method to Optimize the Proportion of Class Label for Prototype Generation in Nearest Neighbor Classification

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 260)

Abstract

Nearest classification with prototype generation methods would be successful on classification in data mining. 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 PSO 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.

Keywords

Particle swarm optimization Prototype generation Evolutionary algorithms Classification 

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Department of Multimedia DesignTajen UniversityTajenTaiwan, Republic of China
  2. 2.Department of Computer Science and Information EngineeringNational Cheng Kung UniversityCheng KungTaiwan, Republic of China
  3. 3.The Institute of Computer and Communication EngineeringNational Cheng Kung UniversityCheng KungTaiwan, Republic of China

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