2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 16 , Issue 1 , PP: 86-101, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Ismael Salih Aref 1 * , Dheyab Salman Ibrahim 2 , Bashar Talib AL-Nuaimi 3
Doi: https://doi.org/10.54216/JISIoT.160108
Feature selection (FS) is a crucial preprocessing step in data mining to eliminate redundant or irrelevant features from high-dimensional data. Many optimization algorithms for FS often lack balance in their search processes. This paper proposes a hybrid algorithm, the Artificial Hummingbird Algorithm based on the Genetic Algorithm (AHA-GA), to address this imbalance and solve the FS problem. The main goal of AHA-GA is to select the most crucial characteristics to improve overall model categorization. The UCI datasets are used to assess the performance of the proposed FS method. The proposed feature selection algorithm was compared with five feature selection optimization algorithms: BWOAHHO, HSGW, WOA-CM, BDA-SA, and ASGW. AHA-GA achieved a classification accuracy of 96% across 18 datasets, which was higher than BWOAHHO (93.2%), HSGW (92.5%), WOA-CM (94.4%), BDA-SA (93%), and ASGW (91.6%). When comparing the proposed AHA-GA algorithm to the results obtained by the other five algorithms in terms of selected attribute size, the average feature sizes were as follows: AHA-GA (15.10889), BWOAHHO (16.74222), HSGW (19.43111), WOA-CM (17.05389), BDA-SA (17.275), and ASGW (19.7585). The statistical and experimental tests demonstrated that the proposed AHA-GA performs better than competitive algorithms in selecting effective features.
Feature Selection , Artificial Hummingbirds algorithm , Genetic Algorithm , Classification
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