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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: | Mahmoud Sobhy; Ahmed Alsawy; Mahmoud Moussa |
| Year: | 2018 |
| Keywords: | molecular classification; Chicken swarm optimization; Particle swarm optimization; feature selection |
| Journal: | Journal of Convergence Information Technology |
| Volume: | Not Available |
| Issue: | Not Available |
| Pages: | Not Available |
| Publisher: | Not Available |
| Local/International: | International |
| Paper Link: | Not Available |
| Full paper | Not Available |
| 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 FS-CSO proves advance over FS-PSO. Also, the two algorithms compared against two previous algorithms, BPSO-BP and BPSO-PSO, that used in feature selection for molecular classification and FS-CSO proves advance over them as well |
