Implication of variation in Ant Colony Optimization Algorithm (ACO) parameters on Feature Subset Size.
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Date
2017-05-20
Journal Title
Journal ISSN
Volume Title
Publisher
Computing and Information System Journal, University of West Scotland
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.
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Citation
Babatunde, R. S., Isiaka, R.M., Adigun, O.J. and Babatunde, A.N. (2017)