| Top-down strategies for hierarchical classification of transposable elements with neural networks FK Nakano, WJ Pinto, GL Pappa, R Cerri 2017 International joint conference on neural networks (IJCNN), 2539-2546, 2017 | 42 | 2017 |
| Machine learning for discovering missing or wrong protein function annotations: a comparison using updated benchmark datasets FK Nakano, M Lietaert, C Vens BMC bioinformatics 20, 1-32, 2019 | 34 | 2019 |
| Active learning for hierarchical multi-label classification FK Nakano, R Cerri, C Vens Data Mining and Knowledge Discovery 34 (5), 1496-1530, 2020 | 33 | 2020 |
| Multi-output tree chaining: An interpretative modelling and lightweight multi-target approach SM Mastelini, VGT da Costa, EJ Santana, FK Nakano, RC Guido, R Cerri, ... Journal of Signal Processing Systems 91, 191-215, 2019 | 32 | 2019 |
| Stacking Methods for Hierarchical Classification FK Nakano, M Saulo, S Barbon, R Cerri 2017 16th IEEE International Conference on Machine Learning and Applications …, 2017 | 22 | 2017 |
| Improving hierarchical classification of transposable elements using deep neural networks FK Nakano, SM Mastelini, S Barbon, R Cerri 2018 International Joint Conference on Neural Networks (IJCNN), 1-8, 2018 | 21 | 2018 |
| Deep tree-ensembles for multi-output prediction FK Nakano, K Pliakos, C Vens Pattern Recognition 121, 108211, 2022 | 15 | 2022 |
| Online extra trees regressor SM Mastelini, FK Nakano, C Vens, ACP de Leon Ferreira IEEE Transactions on Neural Networks and Learning Systems, 2022 | 9 | 2022 |
| Proceedings of the International Joint Conference on Neural Networks FK Nakano, SM Mastelini, S Barbon, R Cerri IEEE, Rio de Janeiro, 2018 | 8 | 2018 |
| Strategies for selection of positive and negative instances in the hierarchical classification of transposable elements BZ Santos, GT Pereira, FK Nakano, R Cerri 2018 7th Brazilian Conference on Intelligent Systems (BRACIS), 420-425, 2018 | 7 | 2018 |
| Predictive bi-clustering trees for hierarchical multi-label classification BZ Santos, FK Nakano, R Cerri, C Vens Machine Learning and Knowledge Discovery in Databases: European Conference …, 2021 | 5 | 2021 |
| BELLATREX: Building explanations through a locally accurate rule extractor K Dedja, FK Nakano, K Pliakos, C Vens Ieee Access 11, 41348-41367, 2023 | 2 | 2023 |
| Explaining a Random Survival Forest by extracting prototype rules K Dedja, FK Nakano, K Pliakos, C Vens Joint European Conference on Machine Learning and Knowledge Discovery in …, 2021 | 2 | 2021 |
| Denoising Auto-Encoders as Feature Extractors in Hierarchical Classification Problems FK Nakano, R Cerri XIV Encontro Nacional de Inteligência Artificial e Computacional, 2017 | 2 | 2017 |
| Leveraging class hierarchy for detecting missing annotations on hierarchical multi-label classification M Romero, FK Nakano, J Finke, C Rocha, C Vens Computers in Biology and Medicine 152, 106423, 2023 | 1 | 2023 |
| Explaining random forest predictions through diverse rules K Dedja, FK Nakano, K Pliakos, C Vens arXiv preprint arXiv:2203.15511, 2022 | 1 | 2022 |
| Predicting adverse long-term neurocognitive outcomes after pediatric intensive care unit admission FK Nakano, K Dulfer, I Vanhorebeek, PJ Wouters, SC Verbruggen, ... Computer Methods and Programs in Biomedicine, 108166, 2024 | | 2024 |
| Estimation of GFR with machine learning models compared to EKFC equation FK Nakano, A Lanot, A Akesson, H Pottel, P Delanaye, U Nyman, J Bjork, ... 2ème Conferénce Intelligence Artificielle Néphrologie, Date: 2023/09/14-2023 …, 2023 | | 2023 |
| PT-MESS: a Problem-Transformation approach for Multi-Event Survival analySis M Venturini, FK Nakano, C Vens SDAIH 2022 Online Proceedings 1, 2023 | | 2023 |
| Active Learning for Survival Analysis with Incrementally Disclosed Label Information K Dedja, FK Nakano, C Vens | | 2023 |
Celine Vensprofessor, Katholieke Universiteit LeuvenVerified email at kuleuven.be
