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Conference Paper
Simplified Swarm Optimization for Life Log Data Mining
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- Authors
- Chang Seok Bae, Wei-Chang Yeh, Yuk Ying Chung
- Issue Date
- 2011-10
- Citation
- International Conference on Information Technology Convergence and Services (ITCS) / FTRA International Conference on Intelligent Robotics, Automations, Telecommunication Facilities, and Applications (IRoA) 2011 (LNEE 107), v.107, pp.583-589
- Publisher
- Springer
- Language
- English
- Type
- Conference Paper
- 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 Na챦ve Bayes. © 2011 Springer Science+Business Media B.V.
- KSP Keywords
- Classification algorithm, Classification tree, Data mining(DM), Data sets, Evolutionary Algorithm(EA), Fast convergence, Life log, Log data, Simplified swarm optimization(SSO), Size reduction, classification accuracy