Tuve Löfström

CoRR, 2023

Calibrated Explanations: with Uncertainty Information and Counterfactuals.

[BibT_eX]

[DOI]

CoRR, 2023

Conformal Prediction for Accuracy Guarantees in Classification with Reject Option.

[BibT_eX]

[DOI]

Proceedings of the Modeling Decisions for Artificial Intelligence, 2023

Applications of Conformal Regression on Real-world Industrial Use Cases using Crepes and MAPIE.

[BibT_eX]

[DOI]

Nasir Uddin

Proceedings of the Conformal and Probabilistic Prediction with Applications, 2023

Tutorial on using Conformal Predictive Systems in KNIME.

[BibT_eX]

[DOI]

Proceedings of the Conformal and Probabilistic Prediction with Applications, 2023

Confidence Classifiers with Guaranteed Accuracy or Precision.

[BibT_eX]

[DOI]

Proceedings of the Conformal and Probabilistic Prediction with Applications, 2023

2022

Rule extraction with guarantees from regression models.

[BibT_eX]

[DOI]

Pattern Recognit., 2022

Product verification using OCR classification and Mondrian conformal prediction.

[BibT_eX]

[DOI]

Rachid Oucheikh

Tobias Pettersson

Expert Syst. Appl., 2022

NLP Cross-Domain Recognition of Retail Products.

[BibT_eX]

[DOI]

Tobias Pettersson

Rachid Oucheikh

Proceedings of the ICMLT 2022: 7th International Conference on Machine Learning Technologies, Rome, Italy, March 11, 2022

Tutorial for using conformal prediction in KNIME.

[BibT_eX]

[DOI]

Artem Ryasik

Proceedings of the Conformal and Probabilistic Prediction with Applications, 2022

Well-Calibrated Rule Extractors.

[BibT_eX]

[DOI]

Niclas Ståhl

Proceedings of the Conformal and Probabilistic Prediction with Applications, 2022

2021

Investigating Normalized Conformal Regressors.

[BibT_eX]

[DOI]

Proceedings of the IEEE Symposium Series on Computational Intelligence, 2021

Well-Calibrated and Sharp Interpretable Multi-Class Models.

[BibT_eX]

[DOI]

Proceedings of the Modeling Decisions for Artificial Intelligence, 2021

Calibrating multi-class models.

[BibT_eX]

[DOI]

Proceedings of the Conformal and Probabilistic Prediction and Applications, 2021

Mondrian conformal predictive distributions.

[BibT_eX]

[DOI]

Proceedings of the Conformal and Probabilistic Prediction and Applications, 2021

2020

Well-calibrated and specialized probability estimation trees.

[BibT_eX]

[DOI]

Proceedings of the 2020 SIAM International Conference on Data Mining, 2020

2019

Efficient Venn predictors using random forests.

[BibT_eX]

[DOI]

Mach. Learn., 2019

Calibrating Probability Estimation Trees using Venn-Abers Predictors.

[BibT_eX]

[DOI]

Proceedings of the 2019 SIAM International Conference on Data Mining, 2019

Customized Interpretable Conformal Regressors.

[BibT_eX]

[DOI]

Proceedings of the 2019 IEEE International Conference on Data Science and Advanced Analytics, 2019

Interpretable and specialized conformal predictors.

[BibT_eX]

[DOI]

Proceedings of the Conformal and Probabilistic Prediction and Applications, 2019

Predictive Modeling of Campaigns to Quantify Performance in Fashion Retail Industry.

[BibT_eX]

[DOI]

Chandadevi Giri

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

2018

Interpretable regression trees using conformal prediction.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2018

Classification with Reject Option Using Conformal Prediction.

[BibT_eX]

[DOI]

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2018

Venn predictors for well-calibrated probability estimation trees.

[BibT_eX]

[DOI]

Proceedings of the 7th Symposium on Conformal and Probabilistic Prediction and Applications, 2018

2017

Accelerating difficulty estimation for conformal regression forests.

[BibT_eX]

[DOI]

Ann. Math. Artif. Intell., 2017

Model-agnostic nonconformity functions for conformal classification.

[BibT_eX]

[DOI]

Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

On the Calibration of Aggregated Conformal Predictors.

[BibT_eX]

[DOI]

Proceedings of the Conformal and Probabilistic Prediction and Applications, 2017

Using Conformal Prediction to Prioritize Compound Synthesis in Drug Discovery.

[BibT_eX]

[DOI]

Proceedings of the Conformal and Probabilistic Prediction and Applications, 2017

2016

Reliable Confidence Predictions Using Conformal Prediction.

[BibT_eX]

[DOI]

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2016

Evaluation of a Variance-Based Nonconformity Measure for Regression Forests.

[BibT_eX]

[DOI]

Proceedings of the Conformal and Probabilistic Prediction with Applications, 2016

2015

On Effectively Creating Ensembles of Classifiers : Studies on Creation Strategies, Diversity and Predicting with Confidence.

[BibT_eX]

[DOI]

PhD thesis, 2015

Bias reduction through conditional conformal prediction.

[BibT_eX]

[DOI]

Intell. Data Anal., 2015

Predicting Adverse Drug Events with Confidence.

[BibT_eX]

[DOI]

Proceedings of the Thirteenth Scandinavian Conference on Artificial Intelligence, 2015

2014

Regression conformal prediction with random forests.

[BibT_eX]

[DOI]

Mach. Learn., 2014

Signed-Error Conformal Regression.

[BibT_eX]

[DOI]

Henrik Linusson

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2014

Efficiency Comparison of Unstable Transductive and Inductive Conformal Classifiers.

[BibT_eX]

[DOI]

Proceedings of the Artificial Intelligence Applications and Innovations, 2014

Rule Extraction with Guaranteed Fidelity.

[BibT_eX]

[DOI]

Proceedings of the Artificial Intelligence Applications and Innovations, 2014

2013

Effective utilization of data in inductive conformal prediction using ensembles of neural networks.

[BibT_eX]

[DOI]

Proceedings of the 2013 International Joint Conference on Neural Networks, 2013

Random brains.

[BibT_eX]

[DOI]

Proceedings of the 2013 International Joint Conference on Neural Networks, 2013

Conformal Prediction Using Decision Trees.

[BibT_eX]

[DOI]

Proceedings of the 2013 IEEE 13th International Conference on Data Mining, 2013

Overproduce-and-select: The grim reality.

[BibT_eX]

[DOI]

Proceedings of the IEEE Symposium on Computational Intelligence and Ensemble Learning, 2013

Evolved decision trees as conformal predictors.

[BibT_eX]

[DOI]

Proceedings of the IEEE Congress on Evolutionary Computation, 2013

2012

Obtaining accurate and comprehensible classifiers using oracle coaching.

[BibT_eX]

[DOI]

Intell. Data Anal., 2012

Producing implicit diversity in ANN ensembles.

[BibT_eX]

[DOI]

Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), 2012

2011

Locally induced predictive models.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Systems, 2011

One tree to explain them all.

[BibT_eX]

[DOI]

Proceedings of the IEEE Congress on Evolutionary Computation, 2011

2010

Comparing methods for generating diverse ensembles of artificial neural networks.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2010

Oracle Coached Decision Trees and Lists.

[BibT_eX]

[DOI]

Proceedings of the Advances in Intelligent Data Analysis IX, 9th International Symposium, 2010

Using Imaginary Ensembles to Select GP Classifiers.

[BibT_eX]

[DOI]

Proceedings of the Genetic Programming, 13th European Conference, 2010

Improving GP classification performance by injection of decision trees.

[BibT_eX]

[DOI]

Proceedings of the IEEE Congress on Evolutionary Computation, 2010

2009

Post-processing Evolved Decision Trees.

[BibT_eX]

[DOI]

Proceedings of the Foundations of Computational Intelligence, 2009

Evaluating Algorithms for Concept Description.

[BibT_eX]

Proceedings of The 2009 International Conference on Data Mining, 2009

Ensemble member selection using multi-objective optimization.

[BibT_eX]

[DOI]

Proceedings of the IEEE Symposium on Computational Intelligence and Data Mining, 2009

Using genetic programming to obtain implicit diversity.

[BibT_eX]

[DOI]

Proceedings of the IEEE Congress on Evolutionary Computation, 2009

2008

Chipper - A Novel Algorithm for Concept Description.

[BibT_eX]

[DOI]

Proceedings of the Tenth Scandinavian Conference on Artificial Intelligence, 2008

Evaluating Standard Techniques for Implicit Diversity.

[BibT_eX]

[DOI]

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2008

The problem with ranking ensembles based on training or validation performance.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2008

On the Use of Accuracy and Diversity Measures for Evaluating and Selecting Ensembles of Classifiers.

[BibT_eX]

[DOI]

Proceedings of the Seventh International Conference on Machine Learning and Applications, 2008

Increasing rule extraction accuracy by post-processing GP trees.

[BibT_eX]

[DOI]

Proceedings of the IEEE Congress on Evolutionary Computation, 2008

2007

The Importance of Diversity in Neural Network Ensembles - An Empirical Investigation.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2007

Empirically investigating the importance of diversity.

[BibT_eX]

[DOI]

Proceedings of the 10th International Conference on Information Fusion, 2007

2006

Building Neural Network Ensembles using Genetic Programming.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2006

Rule Extraction from Opaque Models-- A Slightly Different Perspective.

[BibT_eX]

[DOI]

Proceedings of the Fifth International Conference on Machine Learning and Applications, 2006

Genetically Evolved Trees Representing Ensembles.

[BibT_eX]

[DOI]

Proceedings of the Artificial Intelligence and Soft Computing, 2006

Benefits of relating the Retail Domain and Information Fusion.

[BibT_eX]

[DOI]

Proceedings of the 9th International Conference on Information Fusion, 2006

Introducing GEMS - A Novel Technique for Ensemble Creation.

[BibT_eX]

[DOI]

Proceedings of the Nineteenth International Florida Artificial Intelligence Research Society Conference, 2006

2005

Obtaining Accurate Neural Network Ensembles.

[BibT_eX]

[DOI]

Proceedings of the 2005 International Conference on Computational Intelligence for Modelling Control and Automation (CIMCA 2005), 2005

2004

Rule Extraction by Seeing Through the Model.

[BibT_eX]

[DOI]