| 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 | 81 | 2020 |
| 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 | 15 | 2021 |
| Can metafeatures help improve explanations of prediction models when using behavioral and textual data? Y Ramon, D Martens, T Evgeniou, S Praet Machine Learning, 1-40, 2021 | 9 | 2021 |
| Deep learning on big, sparse, behavioral data S De Cnudde, Y Ramon, D Martens, F Provost Big data 7 (4), 286-307, 2019 | 9 | 2019 |
| 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 | 7 | 2021 |
| 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 | 3 | 2021 |
| Rule-based explanation methods to gain insight into classification models using behavioral data Y Ramon University of Antwerp, 2022 | | 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 |
David MartensUniversity of AntwerpVerified email at uantwerp.be
