Mahmoud Sobhy Ali Hassan
Lecturer
Feature Selection approach for Chemical Compound Classification based on CSO and PSO
Journal of Convergence Information Technology (JCIT) • 2018
Publication Information
Authors
Ahmed Elsawy1, Mahmoud Mousa2, Mahmoud Sobhy3
Keywords
Molecular Classification; Chicken Swarm Optimization; Particle Swarm Optimization;
Feature Selection.
Journal
Journal of Convergence Information Technology (JCIT)
Publisher
Not Available
Volume
13
Issue
Not Available
Pages
60-69
publication.type
International
Paper Link
Open Link
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
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