| Machine learning: the art and science of algorithms that make sense of data P Flach Cambridge university press, 2012 | 2212 | 2012 |
| On graph kernels: Hardness results and efficient alternatives T Gärtner, P Flach, S Wrobel Learning theory and kernel machines: 16th annual conference on learning …, 2003 | 1357 | 2003 |
| CRISP-DM twenty years later: From data mining processes to data science trajectories F Martínez-Plumed, L Contreras-Ochando, C Ferri, J Hernández-Orallo, ... IEEE transactions on knowledge and data engineering 33 (8), 3048-3061, 2019 | 783 | 2019 |
| Multi-instance kernels T Gärtner, PA Flach, A Kowalczyk, AJ Smola ICML 2 (3), 7, 2002 | 740 | 2002 |
| Rule evaluation measures: A unifying view N Lavrač, P Flach, B Zupan International Conference on Inductive Logic Programming, 174-185, 1999 | 617 | 1999 |
| Precision-recall-gain curves: PR analysis done right P Flach, M Kull Advances in neural information processing systems 28, 2015 | 597 | 2015 |
| Subgroup discovery with CN2-SD N Lavrač, B Kavšek, P Flach, L Todorovski Journal of Machine Learning Research 5 (Feb), 153-188, 2004 | 590 | 2004 |
| FACE: feasible and actionable counterfactual explanations R Poyiadzi, K Sokol, R Santos-Rodriguez, T De Bie, P Flach Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 344-350, 2020 | 555 | 2020 |
| Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with dirichlet calibration M Kull, M Perello Nieto, M Kängsepp, T Silva Filho, H Song, P Flach Advances in neural information processing systems 32, 2019 | 549 | 2019 |
| Explainability fact sheets: A framework for systematic assessment of explainable approaches K Sokol, P Flach Proceedings of the 2020 conference on fairness, accountability, and …, 2020 | 502 | 2020 |
| The geometry of ROC space: understanding machine learning metrics through ROC isometrics PA Flach Proceedings of the 20th international conference on machine learning (ICML …, 2003 | 453 | 2003 |
| Learning decision trees using the area under the ROC curve C Ferri, P Flach, J Hernández-Orallo Icml 2, 139-146, 2002 | 439 | 2002 |
| Propositionalization approaches to relational data mining S Kramer, N Lavrač, P Flach Relational data mining, 262-291, 2001 | 427 | 2001 |
| Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining JJF Elder IV, F Fogelman-Soulié, P Flach, M Zaki Association for Computing Machinery (ACM), 2009 | 425 | 2009 |
| A coherent interpretation of AUC as a measure of aggregated classification performance PA Flach, J Hernández-Orallo, C Ferri Proceedings of the 28th International Conference on Machine Learning (ICML …, 2011 | 384 | 2011 |
| Bridging e-health and the internet of things: The sphere project N Zhu, T Diethe, M Camplani, L Tao, A Burrows, N Twomey, D Kaleshi, ... IEEE Intelligent Systems 30 (4), 39-46, 2015 | 346 | 2015 |
| Roc ‘n’rule learning—towards a better understanding of covering algorithms J Fürnkranz, PA Flach Machine learning 58 (1), 39-77, 2005 | 335 | 2005 |
| Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers M Kull, T Silva Filho, P Flach Artificial intelligence and statistics, 623-631, 2017 | 318 | 2017 |
| A unified view of performance metrics: Translating threshold choice into expected classification loss J Hernández-Orallo, P Flach, C Ferri The Journal of Machine Learning Research 13 (1), 2813-2869, 2012 | 316 | 2012 |
| Abduction and Induction: Essays on their relation and integration PA Flach, AC Kakas Kluwer Academic Publishers, 2000 | 310* | 2000 |
Meelis KullProfessor of Artificial Intelligence, University of TartuVerified email at ut.ee
