Publications by authors named "Olatunji A Akinola"
Selecting appropriate feature subsets is a vital task in machine learning. Its main goal is to remove noisy, irrelevant, and redundant feature subsets that could negatively impact the learning model's accuracy and improve classification performance without information loss. Therefore, more advanced optimization methods have been employed to locate the optimal subset of features.
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- * The study introduces a hybrid method called BDMSAO that combines the binary version of DMO and simulated annealing to improve the exploitation capabilities of the original DMO algorithm.
- * Testing on 18 UCI machine learning datasets and three high-dimensional medical datasets demonstrated that BDMSAO outperformed ten other methods, achieving 61.11% overall classification accuracy and 100% accuracy on 9 out of 18 datasets, showcasing its effectiveness in feature selection.