Paper Info
Title
Feature selection with Intelligent Dynamic Swarm and Rough Set
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.
Year
DOI
Venue
2010
10.1016/j.eswa.2010.03.016
Expert Syst. Appl.
Keywords
Field
DocType
feature selection,data mining,particle swarm optimization,new evolutionary algorithm,intelligent dynamic swarm (ids),intelligent dynamic swarm,averagely ids,modified particle swarm optimization,traditional pso,training data,rough set,competitive performance,particle swarm optimization (pso),evolutionary algorithm,genetic algorithm,premature convergence
Particle swarm optimization,Data mining,Swarm behaviour,Premature convergence,Evolutionary algorithm,Computer science,Swarm intelligence,Multi-swarm optimization,Artificial intelligence,Imperialist competitive algorithm,Machine learning,Metaheuristic
Journal
Volume
Issue
ISSN
37
10
Expert Systems With Applications
Citations 
PageRank 
References 
27
0.89
8
Authors
4
Name
Order
Citations
PageRank
Changseok Bae116123.90
Wei-Chang Yeh2107178.35
Yuk Ying Chung321125.47
Sin-Long Liu4351.82