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Improved Whale Optimization with Local-Search Method for Feature Selection

Computers, Materials & Continua 2023年第4期75卷 1371-1389页

作者： Malek Alzaqebah mutasem kalsmadi Sana Jawarneh Jehad Saad Alqurni Mohammed Tayfour Ibrahim Almarashdeh Rami Mustafa A.Mohammad Fahad A.Alghamdi Nahier Aldhafferi Abdullah Alqahtani Khalid A.Alissa Bashar A.Aldeeb Usama A.Badawi Maram Alwohaibi Hayat Alfagham Department of Mathematics College of ScienceImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Basic&Applied Scientific Research Center Imam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of MIS College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Science Community CollegeImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Educational Technology College of EducationImam Abdulrahman Bin Faisal UniversityP.O.Box 1982DammamSaudi Arabia Department of Computer Information Systems College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Deanship of Information and Communication Technology Imam Abdulrahman bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

Various feature selection algorithms are usually employed to improve classification models’overall *** algorithms typically accompany such algorithms to select the optimal set of *** the most currently attractive trends within optimization algorithms are hybrid *** present paper presents two Stages of Local Search models for feature selection based on WOA(Whale Optimization Algorithm)and Great Deluge(GD).GD Algorithm is integrated with the WOA algorithm to improve exploitation by identifying the most promising regions during the *** version is employed using the best solution found by the WOA algorithm and exploited by the GD *** addition,disruptive selection(DS)is employed to select the solutions from the population for local *** is chosen to maintain the diversity of the population via enhancing low and high-quality ***(15)standard benchmark datasets provided by the University of California Irvine(UCI)repository were used in evaluating the proposed approaches’***,a comparison was made with four population-based algorithms as wrapper feature selection methods from the *** proposed techniques have proved their efficiency in enhancing classification accuracy compared to other wrapper ***,the WOA can search effectively in the feature space and choose the most relevant attributes for classification tasks.

关键词： Optimization whale optimization algorithm great deluge algorithm feature selection and classification

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