Conference
Learning from Imbalanced Data Using an Evidential Undersampling-Based Ensemble
| العنوان: | Learning from Imbalanced Data Using an Evidential Undersampling-Based Ensemble |
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| المؤلفون: | Grina, Fares, Elouedi, Zied, Lefevre, Eric |
| المساهمون: | Laboratoire de Recherche Opérationnelle de Décision et de Contrôle de Processus (LARODEC), Université de Tunis-ISG de Tunis, Laboratoire de Génie Informatique et d'Automatique de l'Artois (LGI2A), Université d'Artois (UA) |
| المصدر: | 15th International Conference on Scalable Uncertainty Management, SUM'2022 https://hal.science/hal-03840612 15th International Conference on Scalable Uncertainty Management, SUM'2022, Oct 2022, Paris, France. pp.235-248, ⟨10.1007/978-3-031-18843-5_16⟩ |
| بيانات النشر: | HAL CCSD Springer International Publishing |
| سنة النشر: | 2022 |
| المجموعة: | Université d'Artois: HAL |
| مصطلحات موضوعية: | Imbalanced classification, Ensemble learning, Undersampling, Evidence theory, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] |
| جغرافية الموضوع: | Paris, France |
| الوصف: | International audience ; In many real-world binary classification problems, one class tends to be heavily underrepresented when it consists of far fewer observations than the other class. This results in creating a biased model with undesirable performance. Different techniques, such as undersampling, have been proposed to fix this issue. Ensemble methods have also been proven to be a good strategy to improve the performance of the resulting model in the case of class imbalance. In this paper, we propose an evidential undersampling-based ensemble approach. To alleviate the issue of losing important data, our undersampling technique assigns soft evidential labels to each majority instance, which are later used to discard only the unwanted observations, such as noisy and ambiguous examples. Finally, to improve the final results, the proposed undersampling approach is incorporated into an evidential classifier fusion-based ensemble. The comparative study against wellknown ensemble methods reveal that our method is efficient according to the G-Mean and F-Score measures. |
| نوع الوثيقة: | conference object |
| اللغة: | English |
| Relation: | hal-03840612; https://hal.science/hal-03840612; https://hal.science/hal-03840612/document; https://hal.science/hal-03840612/file/SUM_2022_paper_9310.pdf |
| DOI: | 10.1007/978-3-031-18843-5_16 |
| الاتاحة: | https://hal.science/hal-03840612 https://hal.science/hal-03840612/document https://hal.science/hal-03840612/file/SUM_2022_paper_9310.pdf https://doi.org/10.1007/978-3-031-18843-5_16 |
| Rights: | info:eu-repo/semantics/OpenAccess |
| رقم الانضمام: | edsbas.85A0B4A2 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1007/978-3-031-18843-5_16 |
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