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Simplified Swarm Optimization for Life Log Data Mining

  • Changseok Bae
  • Wei-Chang Yeh
  • Yuk Ying Chung
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 107)

Abstract

This paper proposes a new evolutionary algorithm for life log data mining. The proposed algorithm is based on the particle swarm optimization. The proposed algorithm focuses on three goals such as size reduction of data set, fast convergence, and higher classification accuracy. After executing feature selection method, we employ a method to reduce the size of data set. In order to reduce the processing time, we introduce a simple rule to determine the next movements of the particles. We have applied the proposed algorithm to the UCI data set. The experimental results ascertain that the proposed algorithm show better performance compared to the conventional classification algorithms such as PART, KNN, Classification Tree and Naïve Bayes.

Keywords

Life log Particle Swarm Optimization Simplified Swarm Optimization 

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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Electronics and Telecommunications Research InstituteDaejeonKorea
  2. 2.Advanced Analytics InstituteUniversity of Technology SydneyBroadwayAustralia
  3. 3.School of Information TechnologiesUniversity of SydneySydneyAustralia

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