| The relationship between Precision-Recall and ROC curves J Davis, M Goadrich Proceedings of the 23rd international conference on Machine learning, 233-240, 2006 | 9282 | 2006 |
| Learning from positive and unlabeled data: A survey J Bekker, J Davis Machine learning 109 (4), 719-760, 2020 | 933 | 2020 |
| Actions speak louder than goals: Valuing player actions in soccer T Decroos, L Bransen, J Van Haaren, J Davis Proceedings of the 25th ACM SIGKDD international conference on knowledge …, 2019 | 438 | 2019 |
| Learning first-order horn clauses from web text S Schoenmackers, J Davis, O Etzioni, DS Weld Proceedings of the 2010 conference on empirical methods in natural language …, 2010 | 300 | 2010 |
| Deep transfer via second-order markov logic J Davis, P Domingos Proceedings of the 26th annual international conference on machine learning …, 2009 | 298 | 2009 |
| Lifted probabilistic inference by first-order knowledge compilation G Van den Broeck, N Taghipour, W Meert, J Davis, L De Raedt, T Walsh Proceedings of the Twenty-Second international joint conference on …, 2011 | 253 | 2011 |
| Machine learning with a reject option: A survey K Hendrickx, L Perini, D Van der Plas, W Meert, J Davis Machine Learning 113 (5), 3073-3110, 2024 | 207 | 2024 |
| Estimating the class prior in positive and unlabeled data through decision tree induction J Bekker, J Davis Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 162 | 2018 |
| Beyond the selected completely at random assumption for learning from positive and unlabeled data J Bekker, P Robberechts, J Davis Joint European conference on machine learning and knowledge discovery in …, 2019 | 147 | 2019 |
| Unachievable region in precision-recall space and its effect on empirical evaluation K Boyd, VS Costa, J Davis, CD Page Proceedings of the... International Conference on Machine Learning …, 2012 | 147 | 2012 |
| Automatic discovery of tactics in spatio-temporal soccer match data T Decroos, J Van Haaren, J Davis Proceedings of the 24th acm sigkdd international conference on knowledge …, 2018 | 130 | 2018 |
| Learning Markov network structure with decision trees D Lowd, J Davis 2010 IEEE International Conference on Data Mining, 334-343, 2010 | 121 | 2010 |
| Dtaidistance (version v2) W Meert, K Hendrickx, T Van Craenendonck, P Robberechts, H Blockeel, ... last Accessed, 04-14, 2023 | 116* | 2023 |
| Semi-supervised anomaly detection with an application to water analytics V Vercruyssen, W Meert, G Verbruggen, K Maes, R Baumer, J Davis 2018 ieee international conference on data mining (icdm) 2018, 527-536, 2018 | 116 | 2018 |
| Markov network structure learning: A randomized feature generation approach J Van Haaren, J Davis Proceedings of the AAAI Conference on Artificial Intelligence 26 (1), 1148-1154, 2012 | 112 | 2012 |
| Relationships between the external and internal training load in professional soccer: what can we learn from machine learning? A Jaspers, TO De Beéck, MS Brink, WGP Frencken, F Staes, JJ Davis, ... International journal of sports physiology and performance 13 (5), 625-630, 2018 | 107 | 2018 |
| Probabilistic Computer Model Developed from Clinical Data in National Mammography Database Format to Classify Mammographic Findings1 ES Burnside, J Davis, J Chhatwal, O Alagoz, MJ Lindstrom, BM Geller, ... Radiology 251 (3), 663-672, 2009 | 107 | 2009 |
| View Learning for Statistical Relational Learning: With an Application to Mammography. J Davis, ES Burnside, I de Castro Dutra, D Page, R Ramakrishnan, ... IJCAI, 677-683, 2005 | 91 | 2005 |
| Lifted variable elimination: Decoupling the operators from the constraint language N Taghipour, D Fierens, J Davis, H Blockeel Journal of Artificial Intelligence Research 47, 393-439, 2013 | 89 | 2013 |
| A general anomaly detection framework for fleet-based condition monitoring of machines K Hendrickx, W Meert, Y Mollet, J Gyselinck, B Cornelis, K Gryllias, ... Mechanical Systems and Signal Processing 139, 106585, 2020 | 87 | 2020 |
Wannes MeertDTAI, KU LeuvenVerified email at cs.kuleuven.be
