| publication name | Feature Selection approach for Chemical Compound Classification based on CSO and PSO |
|---|---|
| Authors | Ahmed Elsawy1, Mahmoud Mousa2, Mahmoud Sobhy3 |
| year | 2018 |
| keywords | Molecular Classification; Chicken Swarm Optimization; Particle Swarm Optimization; Feature Selection. |
| journal | Journal of Convergence Information Technology (JCIT) |
| volume | 13 |
| issue | Not Available |
| pages | 60-69 |
| publisher | Not Available |
| Local/International | International |
| Paper Link | http://www.globalcis.org/dl/citation.html?id=JCIT-4412&Search=&op=Title |
| Full paper | download |
| Supplementary materials | Not Available |
Abstract
with the improvement of profoundly efficient chemoinformatics data collection technology, classification of chemical data emerges as a vital topic in chemoinformatics. Towards building highly accurate predictive models for chemical data, here we introduce two feature selection algorithms. The first algorithm based on Chicken swarm optimization (FS-CSO) and the second algorithm based on Particle swarm optimization (FS-PSO). The proposed algorithms were applied to four datasets and FSCSO proves advance over FS-PSO. Also, the two algorithms compared against two previous algorithms, BPSO-BP and BPSO-PSO, that used