g: Profiler—a web-based toolset for functional profiling of gene lists from large-scale experiments J Reimand, M Kull, H Peterson, J Hansen, J Vilo Nucleic acids research 35 (suppl_2), W193-W200, 2007 | 1352 | 2007 |
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 | 465 | 2019 |
Precision-recall-gain curves: PR analysis done right P Flach, M Kull Advances in neural information processing systems 28, 2015 | 425 | 2015 |
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 | 322 | 2019 |
Mining for coexpression across hundreds of datasets using novel rank aggregation and visualization methods P Adler, R Kolde, M Kull, A Tkachenko, H Peterson, J Reimand, J Vilo Genome biology 10, 1-11, 2009 | 176 | 2009 |
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 | 173 | 2017 |
Expression Profiler: next generation—an online platform for analysis of microarray data M Kapushesky, P Kemmeren, AC Culhane, S Durinck, J Ihmels, C Körner, ... Nucleic acids research 32 (suppl_2), W465-W470, 2004 | 155 | 2004 |
ASTD: the alternative splicing and transcript diversity database G Koscielny, V Le Texier, C Gopalakrishnan, V Kumanduri, JJ Riethoven, ... Genomics 93 (3), 213-220, 2009 | 130 | 2009 |
Distribution calibration for regression H Song, T Diethe, M Kull, P Flach Proceedings of the 36th International Conference on Machine Learning (ICML …, 2019 | 114 | 2019 |
Beyond sigmoids: How to obtain well-calibrated probabilities from binary classifiers with beta calibration M Kull, TM Silva Filho, P Flach | 112 | 2017 |
The SPHERE challenge: Activity recognition with multimodal sensor data N Twomey, T Diethe, M Kull, H Song, M Camplani, S Hannuna, X Fafoutis, ... arXiv preprint arXiv:1603.00797, 2016 | 88 | 2016 |
Cost-sensitive boosting algorithms: Do we really need them? N Nikolaou, N Edakunni, M Kull, P Flach, G Brown Machine Learning 104, 359-384, 2016 | 76 | 2016 |
Novel decompositions of proper scoring rules for classification: Score adjustment as precursor to calibration M Kull, P Flach Machine Learning and Knowledge Discovery in Databases: European Conference …, 2015 | 70 | 2015 |
Patterns of dataset shift M Kull, P Flach First international workshop on learning over multiple contexts (LMCE) at …, 2014 | 59 | 2014 |
Comprehensive transcriptome analysis of mouse embryonic stem cell adipogenesis unravels new processes of adipocyte development N Billon, R Kolde, J Reimand, MC Monteiro, M Kull, H Peterson, ... Genome biology 11, 1-16, 2010 | 45 | 2010 |
Classifier calibration: a survey on how to assess and improve predicted class probabilities T Silva Filho, H Song, M Perello-Nieto, R Santos-Rodriguez, M Kull, ... Machine Learning 112 (9), 3211-3260, 2023 | 38 | 2023 |
Fast approximate hierarchical clustering using similarity heuristics M Kull, J Vilo BioData mining 1, 9, 2008 | 34 | 2008 |
Reliability maps: a tool to enhance probability estimates and improve classification accuracy M Kull, PA Flach Machine Learning and Knowledge Discovery in Databases: European Conference …, 2014 | 33 | 2014 |
Probabilistic sensor fusion for ambient assisted living T Diethe, N Twomey, M Kull, P Flach, I Craddock arXiv preprint arXiv:1702.01209, 2017 | 25 | 2017 |
Classifier calibration: How to assess and improve predicted class probabilities: a survey T Silva Filho, H Song, M Perello-Nieto, R Santos-Rodriguez, M Kull, ... arXiv e-prints, arXiv-2112, 2021 | 22 | 2021 |