A robust wrapper-based feature selection technique based on modified teaching learning based optimization with hierarchical learning scheme
Feature selection is a pivotal preprocessing step in deploying machine learning solutions, aimed at removing redundant features from datasets while preserving predictive accuracy. Despite the popular use of wrapper-based feature selection techniques with metaheuristic search algorithms (MSAs) such a...
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| Format: | Article |
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Elsevier
2025-01-01
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| Series: | Engineering Science and Technology, an International Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215098624003215 |
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| author | Li Pan Wy-Liang Cheng Wei Hong Lim Abishek Sharma Vibhu Jately Sew Sun Tiang Amal H. Alharbi El-Sayed M. El-kenawy |
| author_facet | Li Pan Wy-Liang Cheng Wei Hong Lim Abishek Sharma Vibhu Jately Sew Sun Tiang Amal H. Alharbi El-Sayed M. El-kenawy |
| author_sort | Li Pan |
| collection | DOAJ |
| description | Feature selection is a pivotal preprocessing step in deploying machine learning solutions, aimed at removing redundant features from datasets while preserving predictive accuracy. Despite the popular use of wrapper-based feature selection techniques with metaheuristic search algorithms (MSAs) such as Teaching Learning Based Optimization (TLBO), balancing exploration and exploitation in identifying optimal feature subsets remains a fundamental challenge. This paper introduces an advanced wrapper-based feature selection method utilizing an enhanced TLBO variant, termed Modified TLBO with Hierarchical Learning Scheme (MTLBO-HLS). MTLBO-HLS incorporates significant enhancements in both teacher and learner phases to better balance exploration and exploitation, thus improving robustness in solving complex problems like feature selection. Specifically, the Multi-Hierarchical Teacher Phase (MH-TP) divides the MTLBO-HLS population into multiple group tiers based on fitness, where learners are guided by superior peers from better tiers. The Dynamic Peers Interaction Based Learner Phase (DPI-LP) enriches the learning process by enabling interactions with single and multiple elite peers and retaining valuable knowledge across dimensions. Extensive simulations demonstrate MTLBO-HLS’s superior performance in feature selection, consistently identifying the smallest feature subsets and achieving the highest classification accuracies across various datasets. These results highlight its potential to drive industrial innovation through enhanced machine learning model efficiency and accuracy. |
| format | Article |
| id | doaj-art-a799c1fdb91643079d0a1c619f887f4b |
| institution | Kabale University |
| issn | 2215-0986 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Engineering Science and Technology, an International Journal |
| spelling | doaj-art-a799c1fdb91643079d0a1c619f887f4b2024-12-29T04:47:23ZengElsevierEngineering Science and Technology, an International Journal2215-09862025-01-0161101935A robust wrapper-based feature selection technique based on modified teaching learning based optimization with hierarchical learning schemeLi Pan0Wy-Liang Cheng1Wei Hong Lim2Abishek Sharma3Vibhu Jately4Sew Sun Tiang5Amal H. Alharbi6El-Sayed M. El-kenawy7Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia; Zhengzhou Institute of Engineering and Technology, Zhenzhou 450044, ChinaFaculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, MalaysiaFaculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia; Corresponding author.Department of Computer Science and Engineering, Graphic Era Deemed to Be University, Dehradun 248002, IndiaDepartment of Electrical & Electronics Engineering, School of Engineering, University of Petroleum and Energy Studies, Dehra Dun 248007, IndiaFaculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, MalaysiaDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt; School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain; Applied Science Research Center. Applied Science Private University, Amman, Jordan; Jadara University Research Center, Jadara University, JordanFeature selection is a pivotal preprocessing step in deploying machine learning solutions, aimed at removing redundant features from datasets while preserving predictive accuracy. Despite the popular use of wrapper-based feature selection techniques with metaheuristic search algorithms (MSAs) such as Teaching Learning Based Optimization (TLBO), balancing exploration and exploitation in identifying optimal feature subsets remains a fundamental challenge. This paper introduces an advanced wrapper-based feature selection method utilizing an enhanced TLBO variant, termed Modified TLBO with Hierarchical Learning Scheme (MTLBO-HLS). MTLBO-HLS incorporates significant enhancements in both teacher and learner phases to better balance exploration and exploitation, thus improving robustness in solving complex problems like feature selection. Specifically, the Multi-Hierarchical Teacher Phase (MH-TP) divides the MTLBO-HLS population into multiple group tiers based on fitness, where learners are guided by superior peers from better tiers. The Dynamic Peers Interaction Based Learner Phase (DPI-LP) enriches the learning process by enabling interactions with single and multiple elite peers and retaining valuable knowledge across dimensions. Extensive simulations demonstrate MTLBO-HLS’s superior performance in feature selection, consistently identifying the smallest feature subsets and achieving the highest classification accuracies across various datasets. These results highlight its potential to drive industrial innovation through enhanced machine learning model efficiency and accuracy.http://www.sciencedirect.com/science/article/pii/S2215098624003215ClassificationDynamic Peers InteractionFeature SelectionHierarchical LearningTeaching Learning Based Optimization |
| spellingShingle | Li Pan Wy-Liang Cheng Wei Hong Lim Abishek Sharma Vibhu Jately Sew Sun Tiang Amal H. Alharbi El-Sayed M. El-kenawy A robust wrapper-based feature selection technique based on modified teaching learning based optimization with hierarchical learning scheme Engineering Science and Technology, an International Journal Classification Dynamic Peers Interaction Feature Selection Hierarchical Learning Teaching Learning Based Optimization |
| title | A robust wrapper-based feature selection technique based on modified teaching learning based optimization with hierarchical learning scheme |
| title_full | A robust wrapper-based feature selection technique based on modified teaching learning based optimization with hierarchical learning scheme |
| title_fullStr | A robust wrapper-based feature selection technique based on modified teaching learning based optimization with hierarchical learning scheme |
| title_full_unstemmed | A robust wrapper-based feature selection technique based on modified teaching learning based optimization with hierarchical learning scheme |
| title_short | A robust wrapper-based feature selection technique based on modified teaching learning based optimization with hierarchical learning scheme |
| title_sort | robust wrapper based feature selection technique based on modified teaching learning based optimization with hierarchical learning scheme |
| topic | Classification Dynamic Peers Interaction Feature Selection Hierarchical Learning Teaching Learning Based Optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2215098624003215 |
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