BROWSE
Detail
Journal Article
Feature selection with Intelligent Dynamic Swarm and Rough Set
Cited 93 time in 
Share 
Share
- Authors
- Changseok Bae, Wei-Chang Yeh, Yuk Ying Chung, Sin-Long Liu
- Issue Date
- 2010-10
- Citation
- Expert Systems with Applications, v.37, no.10, pp.7026-7032
- ISSN
- 0957-4174
- Publisher
- Elsevier
- Language
- English
- Type
- Journal Article
- Abstract
- Data mining is the most commonly used name to solve problems by analyzing data already present in databases. Feature selection is an important problem in the emerging field of data mining which is aimed at finding a small set of rules from the training data set with predetermined targets. Many approaches, methods and goals including Genetic Algorithms (GA) and swarm-based approaches have been tried out for feature selection in order to these goals. Furthermore, a new technique which named Particle Swarm Optimization (PSO) has been proved to be competitive with GA in several tasks, mainly in optimization areas. However, there are some shortcomings in PSO such as premature convergence. To overcome these, we propose a new evolutionary algorithm called Intelligent Dynamic Swarm (IDS) that is a modified Particle Swarm Optimization. Experimental results states competitive performance of IDS. Due to less computing for swarm generation, averagely IDS is over 30% faster than traditional PSO. © 2010 Elsevier Ltd. All rights reserved.
- KSP Keywords
- Competitive performance, Data mining(DM), Data sets, Evolutionary algorithms(EAs), Feature selection(FS), Genetic Algorithm, Premature Convergence, Rough set(RS), Small set, analyzing data, modified particle swarm optimization