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)

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)

Fast approximate hierarchical clustering using similarity heuristics. - Meelis Kull; Jaak Vilo (BioData Min., 2008)

VisHiC - hierarchical functional enrichment analysis of microarray data. - Darya Krushevskaya; Hedi Peterson; Jüri Reimand; Meelis Kull; Jaak Vilo (Nucleic Acids Res., 2009)

An Evolutionary Model of DNA Substring Distribution. - Meelis Kull; Konstantin Tretyakov; Jaak Vilo (Algorithms and Applications, 2010)

LaCova: A Tree-Based Multi-label Classifier Using Label Covariance as Splitting Criterion. - Reem Al-Otaibi; Meelis Kull; Peter A. Flach (ICMLA, 2014)

Reliability Maps: A Tool to Enhance Probability Estimates and Improve Classification Accuracy. - Meelis Kull; Peter A. Flach (ECML/PKDD (2), 2014)

Rate-Oriented Point-Wise Confidence Bounds for ROC Curves. - Louise A. C. Millard; Meelis Kull; Peter A. Flach (ECML/PKDD (2), 2014)

Reframing in Frequent Pattern Mining. - Chowdhury Farhan Ahmed; Md. Samiullah 001; Nicolas Lachiche; Meelis Kull; Peter A. Flach (ICTAI, 2015)

Precision-Recall-Gain Curves: PR Analysis Done Right. - Peter A. Flach; Meelis Kull (NIPS, 2015)

Versatile Decision Trees for Learning Over Multiple Contexts. - Reem Al-Otaibi; Ricardo B. C. Prudêncio; Meelis Kull; Peter A. Flach (ECML/PKDD (1), 2015)

Novel Decompositions of Proper Scoring Rules for Classification: Score Adjustment as Precursor to Calibration. - Meelis Kull; Peter A. Flach (ECML/PKDD (1), 2015)

Model Reuse with Bike rental Station data (Preamble). - Meelis Kull; Nicolas Lachiche; Adolfo Martínez Usó (DC@PKDD/ECML, 2015)

Declaratively Capturing Local Label Correlations with Multi-Label Trees. - Reem Al-Otaibi; Meelis Kull; Peter A. Flach (ECAI, 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 (ICDM, 2016)

Subgroup Discovery with Proper Scoring Rules. - Hao Song 007; Meelis Kull; Peter A. Flach; Georgios Kalogridis (ECML/PKDD (2), 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)

The SPHERE Challenge: Activity Recognition with Multimodal Sensor Data. - Niall Twomey; Tom Diethe; Meelis Kull; Hao Song 007; Massimo Camplani; Sion L. Hannuna; Xenofon Fafoutis; Ni Zhu; Pete Woznowski; Peter A. Flach; Ian Craddock (CoRR, 2016)

Cost-sensitive boosting algorithms: Do we really need them? - Nikolaos Nikolaou; Narayanan Unny Edakunni; Meelis Kull; Peter A. Flach; Gavin Brown 001 (Mach. Learn., 2016)

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 (AISTATS, 2017)

Probabilistic Sensor Fusion for Ambient Assisted Living. - Tom Diethe; Niall Twomey; Meelis Kull; Peter A. Flach; Ian Craddock (CoRR, 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)

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 007; Emma Tonkin; Niall Twomey; Peter A. Flach (KDD, 2018)

Non-Parametric Calibration of Probabilistic Regression. - Hao Song 007; Meelis Kull; Peter A. Flach (CoRR, 2018)

Distribution calibration for regression. - Hao Song 007; Tom Diethe; Meelis Kull; Peter A. Flach (ICML, 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 007; Peter A. Flach (NeurIPS, 2019)

Non-parametric Bayesian Isotonic Calibration: Fighting Over-Confidence in Binary Classification. - Mari-Liis Allikivi; Meelis Kull (ECML/PKDD (2), 2019)

Shift Happens: Adjusting Classifiers. - Theodore James Thibault Heiser; Mari-Liis Allikivi; Meelis Kull (ECML/PKDD (2), 2019)

HyperStream: a Workflow Engine for Streaming Data. - Tom Diethe; Meelis Kull; Niall Twomey; Kacper Sokol; Hao Song 007; Miquel Perelló-Nieto; Emma Tonkin; Peter A. Flach (CoRR, 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 007; Peter A. Flach (CoRR, 2019)

Correlated daily time series and forecasting in the M4 competition. - Anti Ingel; Novin Shahroudi; Markus Kängsepp; Andre Tättar; Viacheslav Komisarenko; Meelis Kull (CoRR, 2020)

Instance-based Label Smoothing For Better Calibrated Classification Networks. - Mohamed Maher; Meelis Kull (ICMLA, 2021)

Classifier Calibration: How to assess and improve predicted class probabilities: a survey. - Telmo de Menezes e Silva Filho; Hao Song 007; Miquel Perelló-Nieto; Raúl Santos-Rodríguez; Meelis Kull; Peter A. Flach (CoRR, 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)

Evaluating Classifiers' Performance to Detect Attacks in Website Traffic. - Daniel Urda; Nuño Basurto; Meelis Kull; Álvaro Herrero 001 (CISIS-ICEUTE, 2022)

Ethical and Fairness Implications of Model Multiplicity. - Kacper Sokol; Meelis Kull; Jeffrey Chan; Flora Dilys Salim (CoRR, 2022)

Calibrated Perception Uncertainty Across Objects and Regions in Bird's-Eye-View. - Markus Kängsepp; Meelis Kull (CoRR, 2022)

Generality-Training of a Classifier for Improved Calibration in Unseen Contexts. - Bhawani Shankar Leelar; Meelis Kull (ECML/PKDD (5), 2023)

Assuming Locally Equal Calibration Errors for Non-Parametric Multiclass Calibration. - Kaspar Valk; Meelis Kull (Trans. Mach. Learn. Res., 2023)

Classifier calibration: a survey on how to assess and improve predicted class probabilities. - Telmo de Menezes e Silva Filho; Hao Song 007; Miquel Perelló-Nieto; Raúl Santos-Rodríguez; Meelis Kull; Peter A. Flach (Mach. Learn., 2023)

Cautious Calibration in Binary Classification. - Mari-Liis Allikivi; Joonas Järve; Meelis Kull (ECAI, 2024)

Improving Calibration by Relating Focal Loss, Temperature Scaling, and Properness. - Viacheslav Komisarenko; Meelis Kull (ECAI, 2024)

Evaluation of Trajectory Distribution Predictions with Energy Score. - Novin Shahroudi; Mihkel Lepson; Meelis Kull (ICML, 2024)

Enhancing web traffic attacks identification through ensemble methods and feature selection. - Daniel Urda; Branly Martínez; Nuño Basurto; Meelis Kull; Ángel Arroyo; Álvaro Herrero 001 (CoRR, 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)

Cost-sensitive classification with cost uncertainty: do we need surrogate losses? - Viacheslav Komisarenko; Meelis Kull (Mach. Learn., 2025)

On the usefulness of the fit-on-test view on evaluating calibration of classifiers. - Markus Kängsepp; Kaspar Valk; Meelis Kull (Mach. Learn., 2025)