Using particle swarm method to optimize the proportion of class label for prototype generation in nearest neighbor classification

Jui Le Chen, Shih Pang Tseng, Chu Sing Yang

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

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.

Original languageEnglish
Title of host publicationAdvanced Technologies, Embedded and Multimedia for Human-Centric Computing, HumanCom and EMC 2013
Pages239-245
Number of pages7
DOIs
Publication statusPublished - 2014 Feb 17
EventAdvanced Technologies, Embedded and Multimedia for Human-Centric Computing, HumanCom and EMC 2013 - , Taiwan
Duration: 2013 Aug 232013 Aug 25

Publication series

NameLecture Notes in Electrical Engineering
Volume260 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Other

OtherAdvanced Technologies, Embedded and Multimedia for Human-Centric Computing, HumanCom and EMC 2013
Country/TerritoryTaiwan
Period13-08-2313-08-25

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

Fingerprint

Dive into the research topics of 'Using particle swarm method to optimize the proportion of class label for prototype generation in nearest neighbor classification'. Together they form a unique fingerprint.

Cite this