| The effect of hyperparameter tuning on the comparative evaluation of unsupervised anomaly detection methods J Soenen, E Van Wolputte, L Perini, V Vercruyssen, W Meert, J Davis, ... Proceedings of the KDD'21 Workshop on Outlier Detection and Description, 1-9, 2021 | 30 | 2021 |
| A scalable ensemble approach to forecast the electricity consumption of households L Botman, J Soenen, K Theodorakos, A Yurtman, J Bekker, ... IEEE Transactions on Smart Grid 14 (1), 757-768, 2022 | 15 | 2022 |
| Scenario generation of residential electricity consumption through sampling of historical data J Soenen, A Yurtman, T Becker, R Dhulst, K Vanthournout, W Meert, ... Sustainable Energy, Grids and Networks 34, 100985, 2023 | 9 | 2023 |
| Tackling noise in active semi-supervised clustering J Soenen, S Dumančić, T Van Craenendonck, H Blockeel Joint European Conference on Machine Learning and Knowledge Discovery in
, 2020 | 4 | 2020 |
| Semi-Supervised and Explainable Machine Learning with an Application to the Low-Voltage Grid J Soenen, H Blockeel | 1 | 2023 |
| Estimating Dynamic Time Warping Distance Between Time Series with Missing Data A Yurtman, J Soenen, W Meert, H Blockeel Joint European Conference on Machine Learning and Knowledge Discovery in
, 2023 | 1 | 2023 |
| Fast and interpretable electricity consumption scenario generation for individual consumers J Soenen, A Yurtman, T Becker, K Vanthournout, H Blockeel arXiv preprint arXiv:2411.05014, 2024 | | 2024 |
| LinC: Explaining Time Series Clusterings with User-Provided Constraints S Lin, A Yurtman, J Soenen, H Blockeel Joint European Conference on Machine Learning and Knowledge Discovery in
, 2023 | | 2023 |
| Measuring the Dissimilarity Between Time Series with Missing Data A Yurtman, J Soenen, W Meert Springer in the Lecture Notes in Computer Science Series (LNCS), 2023 | | 2023 |
| AD-MERCS: Modeling Normality and Abnormality in Unsupervised Anomaly Detection J Soenen, E Van Wolputte, V Vercruyssen, W Meert, H Blockeel arXiv preprint arXiv:2305.12958, 2023 | | 2023 |
