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Ass. Lect. Mahmoud Sobhy Ali Hassan :: Publications: |
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| Title: | 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: | |
| Full paper | Mahmoud Sobhy Ali Hassan_JCIT4412PPL.pdf |
| 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 |
