Meelis Kull
Cited by
Cited by
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
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
Precision-recall-gain curves: PR analysis done right
P Flach, M Kull
Advances in neural information processing systems 28, 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
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
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
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
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
Distribution calibration for regression
H Song, T Diethe, M Kull, P Flach
Proceedings of the 36th International Conference on Machine Learning (ICML …, 2019
Beyond sigmoids: How to obtain well-calibrated probabilities from binary classifiers with beta calibration
M Kull, TM Silva Filho, P Flach
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
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
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
Patterns of dataset shift
M Kull, P Flach
First international workshop on learning over multiple contexts (LMCE) at …, 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
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
Fast approximate hierarchical clustering using similarity heuristics
M Kull, J Vilo
BioData mining 1, 9, 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
Probabilistic sensor fusion for ambient assisted living
T Diethe, N Twomey, M Kull, P Flach, I Craddock
arXiv preprint arXiv:1702.01209, 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
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Articles 1–20