Enhancing techniques for learning decision trees from imbalanced data I Chaabane, R Guermazi, M Hammami Advances in Data Analysis and Classification 14, 677-745, 2020 | 28 | 2020 |
AECID: Asymmetric entropy for classifying imbalanced data R Guermazi, I Chaabane, M Hammami Information Sciences 467, 373-397, 2018 | 27 | 2018 |
Adapted pruning scheme for the framework of imbalanced data-sets I Chaabane, R Guermazi, M Hammami Procedia computer science 112, 1542-1553, 2017 | 6 | 2017 |
On the detection of performance regression introducing code changes: Experience from the git project D Alshoaibi, I Chaabane, K Hannigan, A Ouni, MW Mkaouer 2022 IEEE 29th Annual Software Technology Conference (STC), 206-217, 2022 | 4 | 2022 |
Impact of sampling on learning asymmetric-entropy decision trees from imbalanced data. I Chaabane, R Guermazi, M Hammami PACIS, 72, 2019 | 3 | 2019 |
Imbalanced learning for robust moving object classification in video surveillance applications RR Boukhriss, I Chaabane, R Guermazi, E Fendri, M Hammami International Conference on Intelligent Systems Design and Applications, 199-209, 2021 | 1 | 2021 |