Name
Abstract | ||
|---|---|---|
Redundant gene selection is an important topic in the field of bioinformatics. This paper proposes a novel algorithm on Redundant Gene Selection by Particle Swarm Optimization (RGS-PSO), which tries to find a compact gene subset with great predictive ability. Compared with the previous works, RGS-PSO measures the redundancy of feature set by the maximum feature inter-correlation, which is more reasonable than those by the averaged inter-correlation. The outstanding performance of RGS-PSO has been examined by the experiments on several real world microarray data sets. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/IJCBS.2009.72 | IJCBS |
Keywords | Field | DocType |
maximum feature inter-correlation,novel algorithm,previous work,redundant feature,great predictive ability,real world microarray data,pso,particle swarm optimisation,important topic,redundant gene selection,feature selection,microarray data set,bioinformatics,outstanding performance,bioinformatic,compact gene subset,particle swarm optimization,microarray data,gene selection | Particle swarm optimization,Gene selection,Feature selection,Pattern recognition,Computer science,Multi-swarm optimization,Feature set,Redundancy (engineering),Microarray analysis techniques,Artificial intelligence,Bioinformatics | Conference |
ISBN | Citations | PageRank |
978-0-7695-3739-9 | 0 | 0.34 |
References | Authors | |
12 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sufen Chen | 1 | 17 | 3.66 |
| Xue-qiang Zeng | 2 | 76 | 7.91 |
| Guo-Zheng Li | 3 | 368 | 42.62 |
| Jack Y. Yang | 4 | 902 | 175.51 |
| Mary Qu Yang | 5 | 933 | 191.35 |