Implication of variation in Ant Colony Optimization Algorithm (ACO) parameters on Feature Subset Size.
| dc.contributor.author | Babatunde, R. S., Isiaka, R.M., Adigun, O.J. and Babatunde, A.N. (2017) | |
| dc.date.accessioned | 2024-10-22T09:46:46Z | |
| dc.date.available | 2024-10-22T09:46:46Z | |
| dc.date.issued | 2017-05-20 | |
| dc.description.abstract | Purpose: The purpose of this research is to apply ant colony optimization (ACO), a nature inspired computational (NIC) technique to achieve optimal feature subset selection, for the purpose of data dimensionality reduction, which will remove redundancy, provide a reduced storage space, improve memory utilization and subsequently, classification task. Design/Methodology/Approach: The ACO feature subset selection process was filter-based, accomplished by using correlation coefficient as the heuristic information which guides the search to determine the pixel (feature) attractiveness. The intensity of the pixel represents the pheromone level . Also, ρ, φ, and are key parameters which were tuned to various arbitrary values at each iteration to determine the best combination of parameters which can result in a reduced feature subset (window size). The procedure was simulated in MATLAB 2012. Findings: The study has been able to demonstrate that experimental results obtained by tuning the parameters of ACO at each run gave a reduced feature subset (window size), at parameter settings of α (0.10), β (0.45), ρ (0.50), φ (1.0) which was salient for efficient face recognition thereby demonstrating the effectiveness and superiority of filter-based over wrapper–based feature selection. Research Limitation: The major limiting factor in this research was time and materials which hindered the further testing and implementation of innovative computational intelligence. Originality/Value: This research work is valuable because it gives the implication of the variation of the ACO parameters which determines the extent to which the optimal solution construction that results in a reduced feature subset can be realized. | |
| dc.identifier.citation | Babatunde, R. S., Isiaka, R.M., Adigun, O.J. and Babatunde, A.N. (2017) | |
| dc.identifier.issn | http://cis.uws.ac.uk | |
| dc.identifier.uri | https://kwasuspace.kwasu.edu.ng/handle/123456789/2506 | |
| dc.publisher | Computing and Information System Journal, University of West Scotland | |
| dc.title | Implication of variation in Ant Colony Optimization Algorithm (ACO) parameters on Feature Subset Size. |