Abstract
Convergence speed is a common issue in most meta-heuristic algorithms including Whale Optimization Algorithm (WOA). In WOA, the control parameter, \(a\), that decreases linearly could affect its convergence speed which resulted in low performance in finding better solutions for feature selection. Thus, a modified WOA (mWOA) is proposed by linearly increasing the control parameter value to solve the issue. mWOA is implemented as a filter-based feature selection method on 4 benchmark high-dimensional datasets (HDD). The performance of the selected features by mWOA was evaluated and compared against original WOA and without feature selection (No FS) using Naïve Bayes, C4.5, K-Nearest Neighbors, and Support Vector Machine classifiers. The features selected by mWOA produced promising classification accuracy as compared to WOA and No FS in 11 out of 20 test cases. Interestingly, mWOA managed to achieve the highest classification accuracy with 90.75% in the dataset that has the largest features. In the future, further improvement of mWOA is vital to overcome the convergence speed issue and decrease the computational time in HDD.
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Acknowledgments
This research was supported by the Ministry of Higher Education (MOHE) through Fundamental Research Grant Scheme (FRGS) (FRGS/1/2018/ICT02/UTHM/02/6).
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Yab, L.Y., Wahid, N., Hamid, R.A. (2022). A Modified Whale Optimization Algorithm as Filter-Based Feature Selection for High Dimensional Datasets. In: Ghazali, R., Mohd Nawi, N., Deris, M.M., Abawajy, J.H., Arbaiy, N. (eds) Recent Advances in Soft Computing and Data Mining. SCDM 2022. Lecture Notes in Networks and Systems, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-00828-3_9
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