| A systematic review of data mining and machine learning for air pollution epidemiology C Bellinger, MS Mohomed Jabbar, O Zaļane, A Osornio-Vargas BMC public health 17, 1-19, 2017 | 273 | 2017 |
| Roadmap on machine learning in electronic structure HJ Kulik, T Hammerschmidt, J Schmidt, S Botti, MAL Marques, M Boley, ... Electronic Structure 4 (2), 023004, 2022 | 127 | 2022 |
| One-class versus binary classification: Which and when? C Bellinger, S Sharma, N Japkowicz 2012 11th international conference on machine learning and applications 2 …, 2012 | 125 | 2012 |
| Synthetic oversampling with the majority class: A new perspective on handling extreme imbalance S Sharma, C Bellinger, B Krawczyk, O Zaiane, N Japkowicz 2018 IEEE international conference on data mining (ICDM), 447-456, 2018 | 116 | 2018 |
| Smotefuna: Synthetic minority over-sampling technique based on furthest neighbour algorithm AS Tarawneh, ABA Hassanat, K Almohammadi, D Chetverikov, ... IEEE Access 8, 59069-59082, 2020 | 72 | 2020 |
| Manifold-based synthetic oversampling with manifold conformance estimation C Bellinger, C Drummond, N Japkowicz Machine Learning 107, 605-637, 2018 | 69 | 2018 |
| Framework for extreme imbalance classification: SWIM—sampling with the majority class C Bellinger, S Sharma, N Japkowicz, OR Zaļane Knowledge and Information Systems 62, 841-866, 2020 | 54 | 2020 |
| Anomaly detection in gamma ray spectra: A machine learning perspective S Sharma, C Bellinger, N Japkowicz, R Berg, K Ungar 2012 IEEE symposium on computational intelligence for security and defence …, 2012 | 47 | 2012 |
| Synthetic oversampling for advanced radioactive threat detection C Bellinger, N Japkowicz, C Drummond 2015 IEEE 14th International Conference on Machine Learning and Applications …, 2015 | 43 | 2015 |
| The class imbalance problem in deep learning K Ghosh, C Bellinger, R Corizzo, P Branco, B Krawczyk, N Japkowicz Machine Learning 113 (7), 4845-4901, 2024 | 41 | 2024 |
| Beyond the boundaries of smote: A framework for manifold-based synthetically oversampling C Bellinger, C Drummond, N Japkowicz Machine Learning and Knowledge Discovery in Databases: European Conference …, 2016 | 40 | 2016 |
| Active learning for one-class classification V Barnabé-Lortie, C Bellinger, N Japkowicz 2015 IEEE 14th international conference on machine learning and applications …, 2015 | 39 | 2015 |
| Sampling a longer life: Binary versus one-class classification revisited C Bellinger, S Sharma, OR Zaıane, N Japkowicz First International Workshop on Learning with Imbalanced Domains: Theory and …, 2017 | 29 | 2017 |
| Interdisciplinary-driven hypotheses on spatial associations of mixtures of industrial air pollutants with adverse birth outcomes J Serrano-Lomelin, CC Nielsen, MSM Jabbar, O Wine, C Bellinger, ... Environment international 131, 104972, 2019 | 28 | 2019 |
| Understanding CNN fragility when learning with imbalanced data D Dablain, KN Jacobson, C Bellinger, M Roberts, NV Chawla Machine Learning 113 (7), 4785-4810, 2024 | 26 | 2024 |
| Undersampling with support vectors for multi-class imbalanced data classification B Krawczyk, C Bellinger, R Corizzo, N Japkowicz 2021 International Joint Conference on Neural Networks (IJCNN), 1-7, 2021 | 25 | 2021 |
| Explainable image analysis for decision support in medical healthcare R Corizzo, Y Dauphin, C Bellinger, E Zdravevski, N Japkowicz 2021 IEEE international conference on big data (big data), 4667-4674, 2021 | 23 | 2021 |
| Active Measure Reinforcement Learning for Observation Cost Minimization. C Bellinger, R Coles, M Crowley, I Tamblyn Canadian AI, 2021 | 22 | 2021 |
| Multi-label classification of anemia patients C Bellinger, A Amid, N Japkowicz, H Victor 2015 IEEE 14th International Conference on Machine Learning and Applications …, 2015 | 20 | 2015 |
| RB-CCR: radial-based combined cleaning and resampling algorithm for imbalanced data classification M Koziarski, C Bellinger, M Woźniak Machine Learning 110, 3059-3093, 2021 | 18 | 2021 |
Nathalie JapkowiczAmerican UniversityEmail verificata su american.edu
