The PDF file you selected should load here if your Web browser has a PDF reader plug-in installed (for example, a recent version of Adobe Acrobat Reader).
If you would like more information about how to print, save, and work with PDFs, Highwire Press provides a helpful Frequently Asked Questions about PDFs.
Alternatively, you can download the PDF file directly to your computer, from where it can be opened using a PDF reader. To download the PDF, click the Download link above.
BibTex Citation Data :
@article{JOIV1509, author = {Li Yu Yab and Noorhaniza Wahid and Rahayu A Hamid}, title = {Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-based Feature Selection: A comparative study}, journal = {JOIV : International Journal on Informatics Visualization}, volume = {7}, number = {2}, year = {2023}, keywords = {Feature selection; metaheuristics; whale optimization algorithm; grey wolf optimizer; control parameter; high-dimensional dataset}, abstract = {Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) are well-perform metaheuristic algorithms used by various researchers in solving feature selection problems. Yet, the slow convergence speed issue in Whale Optimization Algorithm and Grey Wolf Optimizer could demote the performance of feature selection and classification accuracy. Therefore, to overcome this issue, a modified WOA (mWOA) and modified GWO (mGWO) for wrapper-based feature selection were proposed in this study. The proposed mWOA and mGWO were given a new inversed control parameter which was expected to enable more search area for the search agents in the early phase of the algorithms and resulted in a faster convergence speed. The objective of this comparative study is to investigate and compare the effectiveness of the inversed control parameter in the proposed methods against the original algorithms in terms of the number of selected features and the classification accuracy. The proposed methods were implemented in MATLAB where 12 datasets with different dimensionality from the UCI repository were used. kNN was chosen as the classifier to evaluate the classification accuracy of the selected features. Based on the experimental results, mGWO did not show significant improvements in feature reduction and maintained similar accuracy as the original GWO. On the contrary, mWOA outperformed the original WOA in terms of the two criteria mentioned even on high-dimensional datasets. Evaluating the execution time of the proposed methods, utilizing different classifiers, and hybridizing proposed methods with other metaheuristic algorithms to solve feature selection problems would be future works worth exploring.}, issn = {2549-9904}, pages = {477--486}, doi = {10.30630/joiv.7.2.1509}, url = {https://joiv.org/index.php/joiv/article/view/1509} }
Refworks Citation Data :
@article{{JOIV}{1509}, author = {Yab, L., Wahid, N., A Hamid, R.}, title = {Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-based Feature Selection: A comparative study}, journal = {JOIV : International Journal on Informatics Visualization}, volume = {7}, number = {2}, year = {2023}, doi = {10.30630/joiv.7.2.1509}, url = {} }Refbacks
- There are currently no refbacks.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
__________________________________________________________________________
JOIV : International Journal on Informatics Visualization
ISSN 2549-9610 (print) | 2549-9904 (online)
Organized by Society of Visual Informatocs, and Institute of Visual Informatics - UKM and Soft Computing and Data Mining Centre - UTHM
W : http://joiv.org
E : joiv@pnp.ac.id, hidra@pnp.ac.id, rahmat@pnp.ac.id
View JOIV Stats
is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.