Analysis of machine learning strategies for prediction of passing undergraduate admission test
This article primarily focuses on understanding the reasons behind the failure of undergraduate admission seekers using different machine learning (ML) strategies. An operative dataset has been equipped using the least significant attributes to avoid the complexity of the model. The procedure halted...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | International Journal of Information Management Data Insights |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667096822000544 |
| _version_ | 1798016445968285696 |
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| author | Md. Abul Ala Walid S.M. Masum Ahmed Mohammad Zeyad S. M. Saklain Galib Meherun Nesa |
| author_facet | Md. Abul Ala Walid S.M. Masum Ahmed Mohammad Zeyad S. M. Saklain Galib Meherun Nesa |
| author_sort | Md. Abul Ala Walid |
| collection | DOAJ |
| description | This article primarily focuses on understanding the reasons behind the failure of undergraduate admission seekers using different machine learning (ML) strategies. An operative dataset has been equipped using the least significant attributes to avoid the complexity of the model. The procedure halted after obtaining 343 observations with ten different attributes. The predictions are achieved using six immensely used ML techniques. Stratified K-fold cross-validation is mentioned to measure the expertise of proposed models to unsighted data, and Precision, Recall, F-Measure, and AUC Score matrices are determined to assess the efficiency of each model. A comprehensive investigation of this article indicates that the resampling strategy derived from the combination of edited nearest neighbor (ENN) and borderline SVM-based SMOTE and SVM model achieved prominent performance. Additionally, the borderline SVM-based SMOTE and the Adaboost model performs as the second-highest performing model. |
| first_indexed | 2024-04-11T15:50:49Z |
| format | Article |
| id | doaj.art-e32752a6c6fc4e07aaaa1b36bbe266aa |
| institution | Directory Open Access Journal |
| issn | 2667-0968 |
| language | English |
| last_indexed | 2024-04-11T15:50:49Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Information Management Data Insights |
| spelling | doaj.art-e32752a6c6fc4e07aaaa1b36bbe266aa2022-12-22T04:15:19ZengElsevierInternational Journal of Information Management Data Insights2667-09682022-11-0122100111Analysis of machine learning strategies for prediction of passing undergraduate admission testMd. Abul Ala Walid0S.M. Masum Ahmed1Mohammad Zeyad2S. M. Saklain Galib3Meherun Nesa4Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University (BSMRSTU), Gopalganj 8100, Bangladesh; Department of Computer Science and Engineering, Khulna University of Engineering and Technology (KUET), Khulna 9203, Bangladesh; Corresponding authors.Energy and Technology Research Division, Advanced Bioinformatics, Computational Biology and Data Science Laboratory, Bangladesh (ABCD Laboratory, Bangladesh), Chattogram, 4226, Bangladesh; Faculty of Engineering, University of Mons (UMONS), Bd Dolez 31, 7000, Mons, Belgium; School of Engineering and Physical Sciences (EPS), Heriot-Watt University (HWU), EH14 4AS, Edinburgh, Scotland, United Kingdom; Department of Energy Engineering, University of the Basque Country (UPV/EHU), Ingeniero Torres Quevedo Plaza, 1, 48013, Bilbao, Biscay, Spain; Corresponding authors.Energy and Technology Research Division, Advanced Bioinformatics, Computational Biology and Data Science Laboratory, Bangladesh (ABCD Laboratory, Bangladesh), Chattogram, 4226, Bangladesh; School of Engineering and Physical Sciences (EPS), Heriot-Watt University (HWU), EH14 4AS, Edinburgh, Scotland, United Kingdom; Department of Energy Engineering, University of the Basque Country (UPV/EHU), Ingeniero Torres Quevedo Plaza, 1, 48013, Bilbao, Biscay, Spain; School of Science & Technology, International Hellenic University (IHU), 14th km Thessaloniki – N. Moudania, 57001, Thermi, Thessaloniki, GreeceDepartment of Biomedical Engineering, Khulna University of Engineering and Technology (KUET), Khulna, 9203, BangladeshDepartment of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University (BSMRSTU), Gopalganj 8100, BangladeshThis article primarily focuses on understanding the reasons behind the failure of undergraduate admission seekers using different machine learning (ML) strategies. An operative dataset has been equipped using the least significant attributes to avoid the complexity of the model. The procedure halted after obtaining 343 observations with ten different attributes. The predictions are achieved using six immensely used ML techniques. Stratified K-fold cross-validation is mentioned to measure the expertise of proposed models to unsighted data, and Precision, Recall, F-Measure, and AUC Score matrices are determined to assess the efficiency of each model. A comprehensive investigation of this article indicates that the resampling strategy derived from the combination of edited nearest neighbor (ENN) and borderline SVM-based SMOTE and SVM model achieved prominent performance. Additionally, the borderline SVM-based SMOTE and the Adaboost model performs as the second-highest performing model.http://www.sciencedirect.com/science/article/pii/S2667096822000544Machine LearningBalanced DatasetAdaboostSupport Vector Machines (SVM)Precision |
| spellingShingle | Md. Abul Ala Walid S.M. Masum Ahmed Mohammad Zeyad S. M. Saklain Galib Meherun Nesa Analysis of machine learning strategies for prediction of passing undergraduate admission test International Journal of Information Management Data Insights Machine Learning Balanced Dataset Adaboost Support Vector Machines (SVM) Precision |
| title | Analysis of machine learning strategies for prediction of passing undergraduate admission test |
| title_full | Analysis of machine learning strategies for prediction of passing undergraduate admission test |
| title_fullStr | Analysis of machine learning strategies for prediction of passing undergraduate admission test |
| title_full_unstemmed | Analysis of machine learning strategies for prediction of passing undergraduate admission test |
| title_short | Analysis of machine learning strategies for prediction of passing undergraduate admission test |
| title_sort | analysis of machine learning strategies for prediction of passing undergraduate admission test |
| topic | Machine Learning Balanced Dataset Adaboost Support Vector Machines (SVM) Precision |
| url | http://www.sciencedirect.com/science/article/pii/S2667096822000544 |
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