Feature Selection approach for Chemical Compound Classification based on CSO and PSO
Journal of Convergence Information Technology (JCIT) • 2018
معلومات البحث
المؤلفون
Ahmed Elsawy1, Mahmoud Mousa2, Mahmoud Sobhy3
الكلمات المفتاحية
Molecular Classification; Chicken Swarm Optimization; Particle Swarm Optimization;
Feature Selection.
المجلة العلمية
Journal of Convergence Information Technology (JCIT)
الناشر
Not Available
المجلد
13
العدد
Not Available
الصفحات
60-69
publication.type
International
رابط البحث
Open Link
المواد المرفقة
Not Available
الملخص
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
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