Name
Title | ||
|---|---|---|
A Novel Rough Set Reduct Algorithm to Feature Selection Based on Artificial Fish Swarm Algorithm. |
Abstract | ||
|---|---|---|
With the purpose of finding the minimal reduct, this paper proposes a novel feature selection algorithm based on artificial fish swarm algorithm (AFSA) hybrid with rough set (AFSARS). The proposed algorithm searches the minimal reduct in an efficient way to observe the change of the significance of feature subsets and the number of selected features, which is experimentally compared with the quick reduct and other hybrid rough set methods such as genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO) and chaotic binary particle swarm optimization (CBPSO). Experiments demonstrate that the proposed algorithm could achieve the minimal reduct more efficiently than the other methods. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-11897-0_4 | ADVANCES IN SWARM INTELLIGENCE, ICSI 2014, PT II |
Keywords | Field | DocType |
feature selection,rough set,fish swarm algorithm,ant colony optimization,chaotic binary particle swarm optimization | Ant colony optimization algorithms,Reduct,Swarm behaviour,Feature selection,Computer science,Artificial intelligence,Chaotic,Genetic algorithm,Particle swarm optimization,Pattern recognition,Algorithm,Rough set,Machine learning | Conference |
Volume | ISSN | Citations |
8795 | 0302-9743 | 5 |
PageRank | References | Authors |
0.46 | 7 | 3 |