Abstract: |
Feature selection is a well-known prepossessing procedure, and it is considered a challenging
problem in many domains, such as data mining, text mining, medicine, biology, public
health, image processing, data clustering, and others. This paper proposes a novel feature selection
method, called AOAGA, using an improved metaheuristic optimization method that combines the
conventional Arithmetic Optimization Algorithm (AOA) with the Genetic Algorithm (GA) operators.
The AOA is a recently proposed optimizer; it has been employed to solve several benchmark and engineering
problems and has shown a promising performance. The main aim behind the modification
of the AOA is to enhance its search strategies. The conventional version suffers from weaknesses, the
local search strategy, and the trade-off between the search strategies. Therefore, the operators of the
GA can overcome the shortcomings of the conventional AOA. The proposed AOAGA was evaluated
with several well-known benchmark datasets, using several standard evaluation criteria, namely
accuracy, number of selected features, and fitness function. Finally, the results were compared with
the state-of-the-art techniques to prove the performance of the proposed AOAGA method. Moreover,
to further assess the performance of the proposed AOAGA method, two real-world problems containing
gene datasets were used. The findings of this paper illustrated that the proposed AOAGA
method finds new best solutions for several test cases, and it got promising results compared to other
comparative methods published in the literature.
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