Machine Learning Approaches to Advanced Outlier Detection in Psychological Datasets
The core aim of this study is to determine the most effective outlier detection methodologies for multivariate psychological datasets, particularly those derived from Omani students. Due to their complex nature, such datasets demand robust analytical methods. To this end, we employed three sophistic...
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J.J. Strossmayer University of Osijek , Faculty of Electrical Engineering, Computer Science and Information Technology
2025
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| author | Abri K.Al. Sidhu M.S. |
| author2 | 58849949800 |
| author_facet | 58849949800 Abri K.Al. Sidhu M.S. |
| author_sort | Abri K.Al. |
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| content_provider | Universiti Tenaga Nasional |
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| continent | Asia |
| country | Malaysia |
| description | The core aim of this study is to determine the most effective outlier detection methodologies for multivariate psychological datasets, particularly those derived from Omani students. Due to their complex nature, such datasets demand robust analytical methods. To this end, we employed three sophisticated algorithms: local outlier factor (LOF), one-class support vector machine (OCSVM), and isolation forest (IF). Our initial findings showed 155 outliers by both LOF and IF and 147 by OCSVM. A deeper analysis revealed that LOF detected 55 unique outliers based on differences in local density, OCSVM isolated 44 unique outliers utilizing its transformed feature space, and IF identified 76 unique outliers leveraging its tree-based mechanics. Despite these varying results, all methods had a consensus for just 44 outliers. Employing ensemble techniques, both averaging and voting methods identified 155 outliers, whereas the weighted method highlighted 151, with a consensus of 150 outliers across the board. In conclusion, while individual algorithms provide distinct perspectives, ensemble techniques enhance the accuracy and consistency of outlier detection. This underscores the necessity of using multiple algorithms with ensemble techniques in analyzing psychological datasets, facilitating a richer comprehension of inherent data structures. ? 2024, J.J. Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology. All rights reserved. |
| format | Article |
| id | my.uniten.dspace-36872 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2025 |
| publisher | J.J. Strossmayer University of Osijek , Faculty of Electrical Engineering, Computer Science and Information Technology |
| record_format | dspace |
| spelling | my.uniten.dspace-368722025-03-03T15:45:22Z Machine Learning Approaches to Advanced Outlier Detection in Psychological Datasets Abri K.Al. Sidhu M.S. 58849949800 56259597000 The core aim of this study is to determine the most effective outlier detection methodologies for multivariate psychological datasets, particularly those derived from Omani students. Due to their complex nature, such datasets demand robust analytical methods. To this end, we employed three sophisticated algorithms: local outlier factor (LOF), one-class support vector machine (OCSVM), and isolation forest (IF). Our initial findings showed 155 outliers by both LOF and IF and 147 by OCSVM. A deeper analysis revealed that LOF detected 55 unique outliers based on differences in local density, OCSVM isolated 44 unique outliers utilizing its transformed feature space, and IF identified 76 unique outliers leveraging its tree-based mechanics. Despite these varying results, all methods had a consensus for just 44 outliers. Employing ensemble techniques, both averaging and voting methods identified 155 outliers, whereas the weighted method highlighted 151, with a consensus of 150 outliers across the board. In conclusion, while individual algorithms provide distinct perspectives, ensemble techniques enhance the accuracy and consistency of outlier detection. This underscores the necessity of using multiple algorithms with ensemble techniques in analyzing psychological datasets, facilitating a richer comprehension of inherent data structures. ? 2024, J.J. Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology. All rights reserved. Final 2025-03-03T07:45:22Z 2025-03-03T07:45:22Z 2024 Article 10.32985/ijeces.15.1.2 2-s2.0-85183437231 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183437231&doi=10.32985%2fijeces.15.1.2&partnerID=40&md5=a041e0bcf4cb8bfba3b9e3b59fa25b44 https://irepository.uniten.edu.my/handle/123456789/36872 15 1 13 20 All Open Access; Gold Open Access; Green Open Access J.J. Strossmayer University of Osijek , Faculty of Electrical Engineering, Computer Science and Information Technology Scopus |
| spellingShingle | Abri K.Al. Sidhu M.S. Machine Learning Approaches to Advanced Outlier Detection in Psychological Datasets |
| title | Machine Learning Approaches to Advanced Outlier Detection in Psychological Datasets |
| title_full | Machine Learning Approaches to Advanced Outlier Detection in Psychological Datasets |
| title_fullStr | Machine Learning Approaches to Advanced Outlier Detection in Psychological Datasets |
| title_full_unstemmed | Machine Learning Approaches to Advanced Outlier Detection in Psychological Datasets |
| title_short | Machine Learning Approaches to Advanced Outlier Detection in Psychological Datasets |
| title_sort | machine learning approaches to advanced outlier detection in psychological datasets |
| url_provider | http://dspace.uniten.edu.my/ |