| Parameter control in evolutionary algorithms: Trends and challenges G Karafotias, M Hoogendoorn, ÁE Eiben IEEE Transactions on Evolutionary Computation 19 (2), 167-187, 2014 | 522 | 2014 |
| Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy LM Fleuren, TLT Klausch, CL Zwager, LJ Schoonmade, T Guo, ... Intensive care medicine 46, 383-400, 2020 | 445 | 2020 |
| Modelling collective decision making in groups and crowds: Integrating social contagion and interacting emotions, beliefs and intentions T Bosse, M Hoogendoorn, MCA Klein, J Treur, CN Van Der Wal, ... Autonomous Agents and Multi-Agent Systems 27, 52-84, 2013 | 197 | 2013 |
| Ckconv: Continuous kernel convolution for sequential data DW Romero, A Kuzina, EJ Bekkers, JM Tomczak, M Hoogendoorn arXiv preprint arXiv:2102.02611, 2021 | 117 | 2021 |
| Attentive group equivariant convolutional networks D Romero, E Bekkers, J Tomczak, M Hoogendoorn International Conference on Machine Learning, 8188-8199, 2020 | 86 | 2020 |
| Flexconv: Continuous kernel convolutions with differentiable kernel sizes DW Romero, RJ Bruintjes, JM Tomczak, EJ Bekkers, M Hoogendoorn, ... arXiv preprint arXiv:2110.08059, 2021 | 84 | 2021 |
| The triangle of life: Evolving robots in real-time and real-space AE Eiben, N Bredeche, M Hoogendoorn, J Stradner, J Timmis, A Tyrrell, ... European conference on artificial life (ECAL-2013), 1-8, 2013 | 79 | 2013 |
| Modeling centralized organization of organizational change M Hoogendoorn, CM Jonker, MC Schut, J Treur Computational and Mathematical Organization Theory 13, 147-184, 2007 | 67 | 2007 |
| Deep learning-based energy disaggregation and on/off detection of household appliances J Jiang, Q Kong, MD Plumbley, N Gilbert, M Hoogendoorn, DM Roijers ACM Transactions on Knowledge Discovery from Data (TKDD) 15 (3), 1-21, 2021 | 65 | 2021 |
| Machine learning for the quantified self M Hoogendoorn, B Funk On the art of learning from sensory data, 2018 | 61 | 2018 |
| Formal modelling and comparing of disaster plans M Hoogedoorn, C Jonker, V Popova, A Sharpanskykh, L Xu | 61 | 2005 |
| Predicting social anxiety treatment outcome based on therapeutic email conversations M Hoogendoorn, T Berger, A Schulz, T Stolz, P Szolovits IEEE journal of biomedical and health informatics 21 (5), 1449-1459, 2016 | 60 | 2016 |
| Predictive modeling of colorectal cancer using a dedicated pre-processing pipeline on routine electronic medical records R Kop, M Hoogendoorn, A Ten Teije, FL Büchner, P Slottje, LMG Moons, ... Computers in biology and medicine 76, 30-38, 2016 | 57 | 2016 |
| Utilizing uncoded consultation notes from electronic medical records for predictive modeling of colorectal cancer M Hoogendoorn, P Szolovits, LMG Moons, ME Numans Artificial intelligence in medicine 69, 53-61, 2016 | 57 | 2016 |
| Generic parameter control with reinforcement learning G Karafotias, AE Eiben, M Hoogendoorn Proceedings of the 2014 annual conference on genetic and evolutionary …, 2014 | 56 | 2014 |
| Agent-based analysis of patterns in crowd behaviour involving contagion of mental states T Bosse, M Hoogendoorn, MCA Klein, J Treur, CN Van Der Wal Modern Approaches in Applied Intelligence: 24th International Conference on …, 2011 | 50 | 2011 |
| Modeling situation awareness in human-like agents using mental models M Hoogendoorn, RM van Lambalgen, J Treur Twenty-Second International Joint Conference on Artificial Intelligence, 2011 | 48 | 2011 |
| Modeling the dynamics of mood and depression F Both, M Hoogendoorn, M Klein, J Treur ECAI 2008, 266-270, 2008 | 48 | 2008 |
| Predictive modeling in e-mental health: a common language framework D Becker, W van Breda, B Funk, M Hoogendoorn, J Ruwaard, H Riper Internet interventions 12, 57-67, 2018 | 46 | 2018 |
| Reinforcement learning for personalization: A systematic literature review F Den Hengst, EM Grua, A el Hassouni, M Hoogendoorn Data Science 3 (2), 107-147, 2020 | 45 | 2020 |
