A comparison of instance-level counterfactual explanation algorithms for behavioral and textual data: SEDC, LIME-C and SHAP-C Y Ramon, D Martens, F Provost, T Evgeniou Advances in Data Analysis and Classification 14, 801-819, 2020 | 115 | 2020 |
Explainable AI for psychological profiling from behavioral data: An application to big five personality predictions from financial transaction records Y Ramon, RA Farrokhnia, SC Matz, D Martens Information 12 (12), 518, 2021 | 24 | 2021 |
Understanding consumer preferences for explanations generated by XAI algorithms Y Ramon, T Vermeire, O Toubia, D Martens, T Evgeniou arXiv preprint arXiv:2107.02624, 2021 | 21 | 2021 |
Deep learning on big, sparse, behavioral data S De Cnudde, Y Ramon, D Martens, F Provost Big data 7 (4), 286-307, 2019 | 12 | 2019 |
Can metafeatures help improve explanations of prediction models when using behavioral and textual data? Y Ramon, D Martens, T Evgeniou, S Praet Machine Learning 113 (7), 4245-4284, 2024 | 11 | 2024 |
Metafeatures-based rule-extraction for classifiers on behavioral and textual data Y Ramon, D Martens, T Evgeniou, S Praet arXiv preprint arXiv:2003.04792, 2020 | 7 | 2020 |
How should artificial intelligence explain itself? understanding preferences for explanations generated by XAI algorithms Y Ramon, T Vermeire, D Martens, T Evgeniou, O Toubia Understanding Preferences for Explanations Generated by XAI Algorithms (June …, 2021 | 5 | 2021 |
The Impact of Cloaking Digital Footprints on User Privacy and Personalization S Goethals, S Matz, F Provost, Y Ramon, D Martens arXiv preprint arXiv:2312.15000, 2023 | 1 | 2023 |
Rule-based explanation methods to gain insight into classification models using behavioral data Y Ramon University of Antwerp, 2022 | 1 | 2022 |
Explainable AI for Psychological Profiling from Digital Footprints: A Case Study of Big Five Personality Predictions from Spending Data DM Yanou Ramon, Sandra C. Matz, R.A. Farrokhnia https://arxiv.org/abs/2111.06908, 2021 | | 2021 |