Meelis Kull

According to our database, Meelis Kull authored at least 41 papers between 2004 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Predicting the Activity of Unidentified Chemicals in Complementary Bioassays from the HRMS Data to Pinpoint Potential Endocrine Disruptors.

Ida Rahu, Meelis Kull, Anneli Kruve

J. Chem. Inf. Model., 2024

2023

Classifier calibration: a survey on how to assess and improve predicted class probabilities.

Telmo de Menezes e Silva Filho, Hao Song, Miquel Perelló-Nieto, Raúl Santos-Rodríguez, Meelis Kull, Peter A. Flach

Mach. Learn., September, 2023

Generality-Training of a Classifier for Improved Calibration in Unseen Contexts.

Bhawani Shankar Leelar, Meelis Kull

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

2022

Calibrated Perception Uncertainty Across Objects and Regions in Bird's-Eye-View.

Markus Kängsepp, Meelis Kull

CoRR, 2022

On the Usefulness of the Fit-on-the-Test View on Evaluating Calibration of Classifiers.

Markus Kängsepp, Kaspar Valk, Meelis Kull

CoRR, 2022

Ethical and Fairness Implications of Model Multiplicity.

Kacper Sokol, Meelis Kull, Jeffrey Chan, Flora Dilys Salim

CoRR, 2022

Evaluating Classifiers' Performance to Detect Attacks in Website Traffic.

Daniel Urda, Nuño Basurto, Meelis Kull, Álvaro Herrero

Proceedings of the International Joint Conference 15th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2022) 13th International Conference on EUropean Transnational Education (ICEUTE 2022), 2022

2021

CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories.

Fernando Martínez-Plumed, Lidia Contreras Ochando, Cèsar Ferri, José Hernández-Orallo, Meelis Kull, Nicolas Lachiche, María José Ramírez-Quintana, Peter A. Flach

IEEE Trans. Knowl. Data Eng., 2021

Classifier Calibration: How to assess and improve predicted class probabilities: a survey.

Telmo de Menezes e Silva Filho, Hao Song, Miquel Perelló-Nieto, Raúl Santos-Rodríguez, Meelis Kull, Peter A. Flach

CoRR, 2021

Instance-based Label Smoothing For Better Calibrated Classification Networks.

Mohamed Maher, Meelis Kull

Proceedings of the 20th IEEE International Conference on Machine Learning and Applications, 2021

2020

Correlated daily time series and forecasting in the M4 competition.

Anti Ingel, Novin Shahroudi, Markus Kängsepp, Andre Tättar, Viacheslav Komisarenko, Meelis Kull

CoRR, 2020

2019

Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration.

Meelis Kull, Miquel Perelló-Nieto, Markus Kängsepp, Telmo de Menezes e Silva Filho, Hao Song, Peter A. Flach

CoRR, 2019

HyperStream: a Workflow Engine for Streaming Data.

Tom Diethe, Meelis Kull, Niall Twomey, Kacper Sokol, Hao Song, Miquel Perelló-Nieto, Emma Tonkin, Peter A. Flach

CoRR, 2019

Shift Happens: Adjusting Classifiers.

Theodore James Thibault Heiser, Mari-Liis Allikivi, Meelis Kull

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

Non-parametric Bayesian Isotonic Calibration: Fighting Over-Confidence in Binary Classification.

Mari-Liis Allikivi, Meelis Kull

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration.

Meelis Kull, Miquel Perelló-Nieto, Markus Kängsepp, Telmo de Menezes e Silva Filho, Hao Song, Peter A. Flach

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Distribution calibration for regression.

Hao Song, Tom Diethe, Meelis Kull, Peter A. Flach

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Non-Parametric Calibration of Probabilistic Regression.

Hao Song, Meelis Kull, Peter A. Flach

CoRR, 2018

Releasing eHealth Analytics into the Wild: Lessons Learnt from the SPHERE Project.

Tom Diethe, Mike Holmes, Meelis Kull, Miquel Perelló-Nieto, Kacper Sokol, Hao Song, Emma Tonkin, Niall Twomey, Peter A. Flach

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

2017

CASP-DM: Context Aware Standard Process for Data Mining.

Fernando Martínez-Plumed, Lidia Contreras Ochando, César Ferri, Peter A. Flach, José Hernández-Orallo, Meelis Kull, Nicolas Lachiche, María José Ramírez-Quintana

CoRR, 2017

Probabilistic Sensor Fusion for Ambient Assisted Living.

Tom Diethe, Niall Twomey, Meelis Kull, Peter A. Flach, Ian Craddock

CoRR, 2017

Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers.

Meelis Kull, Telmo de Menezes e Silva Filho, Peter A. Flach

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Cost-sensitive boosting algorithms: Do we really need them?

Nikolaos Nikolaou, Narayanan Unny Edakunni, Meelis Kull, Peter A. Flach, Gavin Brown

Mach. Learn., 2016

The SPHERE Challenge: Activity Recognition with Multimodal Sensor Data.

Niall Twomey, Tom Diethe, Meelis Kull, Hao Song, Massimo Camplani, Sion L. Hannuna, Xenofon Fafoutis, Ni Zhu, Pete Woznowski, Peter A. Flach, Ian Craddock

CoRR, 2016

Reframing in context: A systematic approach for model reuse in machine learning.

José Hernández-Orallo, Adolfo Martínez Usó, Ricardo B. C. Prudêncio, Meelis Kull, Peter A. Flach, Chowdhury Farhan Ahmed, Nicolas Lachiche

AI Commun., 2016

Subgroup Discovery with Proper Scoring Rules.

Hao Song, Meelis Kull, Peter A. Flach, Georgios Kalogridis

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2016

Background Check: A General Technique to Build More Reliable and Versatile Classifiers.

Miquel Perelló-Nieto, Telmo de Menezes e Silva Filho, Meelis Kull, Peter A. Flach

Proceedings of the IEEE 16th International Conference on Data Mining, 2016

Declaratively Capturing Local Label Correlations with Multi-Label Trees.

Reem Al-Otaibi, Meelis Kull, Peter A. Flach

Proceedings of the ECAI 2016 - 22nd European Conference on Artificial Intelligence, 29 August-2 September 2016, The Hague, The Netherlands, 2016

2015

Model Reuse with Bike rental Station data (Preamble).

Meelis Kull, Nicolas Lachiche, Adolfo Martínez Usó

Proceedings of the ECML/PKDD 2015 Discovery Challenges co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2015), 2015

Novel Decompositions of Proper Scoring Rules for Classification: Score Adjustment as Precursor to Calibration.

Meelis Kull, Peter A. Flach

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

Versatile Decision Trees for Learning Over Multiple Contexts.

Reem Al-Otaibi, Ricardo B. C. Prudêncio, Meelis Kull, Peter A. Flach

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

Precision-Recall-Gain Curves: PR Analysis Done Right.

Peter A. Flach, Meelis Kull

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Reframing in Frequent Pattern Mining.

Chowdhury Farhan Ahmed, Md. Samiullah, Nicolas Lachiche, Meelis Kull, Peter A. Flach

Proceedings of the 27th IEEE International Conference on Tools with Artificial Intelligence, 2015

2014

Rate-Oriented Point-Wise Confidence Bounds for ROC Curves.

Louise A. C. Millard, Meelis Kull, Peter A. Flach

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2014

Reliability Maps: A Tool to Enhance Probability Estimates and Improve Classification Accuracy.

Meelis Kull, Peter A. Flach

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2014

LaCova: A Tree-Based Multi-label Classifier Using Label Covariance as Splitting Criterion.

Reem Al-Otaibi, Meelis Kull, Peter A. Flach

Proceedings of the 13th International Conference on Machine Learning and Applications, 2014

2010

An Evolutionary Model of DNA Substring Distribution.

Meelis Kull, Konstantin Tretyakov, Jaak Vilo

Proceedings of the Algorithms and Applications, 2010

2009

VisHiC - hierarchical functional enrichment analysis of microarray data.

Darya Krushevskaya, Hedi Peterson, Jüri Reimand, Meelis Kull, Jaak Vilo

Nucleic Acids Res., 2009

2008

Fast approximate hierarchical clustering using similarity heuristics.

Meelis Kull, Jaak Vilo

BioData Min., 2008

2007

g: Profiler - a web-based toolset for functional profiling of gene lists from large-scale experiments.

Jüri Reimand, Meelis Kull, Hedi Peterson, Jaanus Hansen, Jaak Vilo

Nucleic Acids Res., 2007

2004

Expression Profiler: next generation - an online platform for analysis of microarray data.

Misha Kapushesky, Patrick Kemmeren, Aedín C. Culhane, Steffen Durinck, Jan Ihmels, Christine Körner, Meelis Kull, Aurora Torrente, Ugis Sarkans, Jaak Vilo, Alvis Brazma

Nucleic Acids Res., 2004