Parallel Metaheuristic Algorithms for Solving Imbalanced Data Classification Problems
An imbalanced classification problem is one in which the distribution of instances across defined classes is uneven or biased in one direction or another. In data mining, the probabilistic neural network (PNN) classifier is a well-known technology that has been successfully used to solve a variety o...
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10283817/ |
| _version_ | 1827787706059456512 |
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| author | Mohammed Alweshah Muder Almiani Saleh Alkhalaileh Sofian Kassaymeh Essa Abdullah Hezzam Waleed Alomoush |
| author_facet | Mohammed Alweshah Muder Almiani Saleh Alkhalaileh Sofian Kassaymeh Essa Abdullah Hezzam Waleed Alomoush |
| author_sort | Mohammed Alweshah |
| collection | DOAJ |
| description | An imbalanced classification problem is one in which the distribution of instances across defined classes is uneven or biased in one direction or another. In data mining, the probabilistic neural network (PNN) classifier is a well-known technology that has been successfully used to solve a variety of classification difficulties. On the other hand, metaheuristic optimization approaches offer an excellent means by which to deal with this problem. Therefore, this work combines two metaheuristic algorithms–the Ali Baba and the Forty Thieves (AFT) algorithm and the Water Strider Algorithm (WSA)–in order to alter the weights of a PNN classifier for imbalanced datasets. This article introduces a self-contained multiple-search approach for parallel metaheuristics that may be used in a variety of situations. Most implementations begin many search processes, all of which utilize the same search algorithm, with a set of starting parameters that are all generated separately. Most implementations pick a processor to collect data and verify the data for compliance with some stopping criteria, with the latter being the default. In the proposed AFT-WSA parallel method, the two algorithms begin simultaneously, and the fitness value is communicated in each iteration to find the best classification accuracy in the smallest number of iterations, thereby allowing the weight of the PNN classifier to be adjusted. In this study, ten imbalanced public datasets were used to test the performance of the proposed approach in terms of classification accuracy, standard deviation, and F-measure. |
| first_indexed | 2024-03-11T16:53:04Z |
| format | Article |
| id | doaj.art-1e753db664224938847c698a0abf044b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T16:53:04Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1e753db664224938847c698a0abf044b2023-10-20T23:00:31ZengIEEEIEEE Access2169-35362023-01-011111444311445810.1109/ACCESS.2023.332406110283817Parallel Metaheuristic Algorithms for Solving Imbalanced Data Classification ProblemsMohammed Alweshah0https://orcid.org/0000-0002-3724-5111Muder Almiani1Saleh Alkhalaileh2Sofian Kassaymeh3https://orcid.org/0000-0003-0586-1961Essa Abdullah Hezzam4https://orcid.org/0000-0003-1024-5792Waleed Alomoush5Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, JordanDepartment of Management Information System (MIS), Gulf University for Science and Technology, Kuwait City, KuwaitPrince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, JordanSoftware Engineering Department, Faculty of Information Technology, Aqaba University of Technology, Aqaba, JordanDepartment of Information Systems, College of Computer Science and Engineering, Taibah University, Medina, Saudi ArabiaSchool of Computing, Skyline University College, Sharjah, United Arab EmiratesAn imbalanced classification problem is one in which the distribution of instances across defined classes is uneven or biased in one direction or another. In data mining, the probabilistic neural network (PNN) classifier is a well-known technology that has been successfully used to solve a variety of classification difficulties. On the other hand, metaheuristic optimization approaches offer an excellent means by which to deal with this problem. Therefore, this work combines two metaheuristic algorithms–the Ali Baba and the Forty Thieves (AFT) algorithm and the Water Strider Algorithm (WSA)–in order to alter the weights of a PNN classifier for imbalanced datasets. This article introduces a self-contained multiple-search approach for parallel metaheuristics that may be used in a variety of situations. Most implementations begin many search processes, all of which utilize the same search algorithm, with a set of starting parameters that are all generated separately. Most implementations pick a processor to collect data and verify the data for compliance with some stopping criteria, with the latter being the default. In the proposed AFT-WSA parallel method, the two algorithms begin simultaneously, and the fitness value is communicated in each iteration to find the best classification accuracy in the smallest number of iterations, thereby allowing the weight of the PNN classifier to be adjusted. In this study, ten imbalanced public datasets were used to test the performance of the proposed approach in terms of classification accuracy, standard deviation, and F-measure.https://ieeexplore.ieee.org/document/10283817/Ali Baba and the Forty Thieves algorithmimbalanced dataparallel metaheuristicPNN classifierwater strider algorithm |
| spellingShingle | Mohammed Alweshah Muder Almiani Saleh Alkhalaileh Sofian Kassaymeh Essa Abdullah Hezzam Waleed Alomoush Parallel Metaheuristic Algorithms for Solving Imbalanced Data Classification Problems IEEE Access Ali Baba and the Forty Thieves algorithm imbalanced data parallel metaheuristic PNN classifier water strider algorithm |
| title | Parallel Metaheuristic Algorithms for Solving Imbalanced Data Classification Problems |
| title_full | Parallel Metaheuristic Algorithms for Solving Imbalanced Data Classification Problems |
| title_fullStr | Parallel Metaheuristic Algorithms for Solving Imbalanced Data Classification Problems |
| title_full_unstemmed | Parallel Metaheuristic Algorithms for Solving Imbalanced Data Classification Problems |
| title_short | Parallel Metaheuristic Algorithms for Solving Imbalanced Data Classification Problems |
| title_sort | parallel metaheuristic algorithms for solving imbalanced data classification problems |
| topic | Ali Baba and the Forty Thieves algorithm imbalanced data parallel metaheuristic PNN classifier water strider algorithm |
| url | https://ieeexplore.ieee.org/document/10283817/ |
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