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2020 – today
- 2024
[j11]Helena Löfström
, Tuwe Löfström, Ulf Johansson, Cecilia Sönströd:
Calibrated explanations: With uncertainty information and counterfactuals. Expert Syst. Appl. 246: 123154 (2024)- 2023
- [c97]Amr Alkhatib, Henrik Boström, Sofiane Ennadir, Ulf Johansson:
Approximating Score-based Explanation Techniques Using Conformal Regression. COPA 2023: 450-469 - [c96]Ulf Johansson, Cecilia Sönströd, Tuwe Löfström, Henrik Boström:
Confidence Classifiers with Guaranteed Accuracy or Precision. COPA 2023: 513-533 - [c95]Tuwe Löfström, Alexander Bondaletov, Artem Ryasik, Henrik Boström, Ulf Johansson:
Tutorial on using Conformal Predictive Systems in KNIME. COPA 2023: 602-620 - [c94]Ulf Johansson, Tuwe Löfström, Cecilia Sönströd, Helena Löfström:
Conformal Prediction for Accuracy Guarantees in Classification with Reject Option. MDAI 2023: 133-145 - [i5]Helena Löfström, Tuwe Löfström, Ulf Johansson, Cecilia Sönströd:
Calibrated Explanations: with Uncertainty Information and Counterfactuals. CoRR abs/2305.02305 (2023) - [i4]Ulf Johansson, Tuwe Löfström, Cecilia Sönströd:
Well-Calibrated Probabilistic Predictive Maintenance using Venn-Abers. CoRR abs/2306.06642 (2023) - [i3]Amr Alkhatib, Henrik Boström, Sofiane Ennadir, Ulf Johansson:
Approximating Score-based Explanation Techniques Using Conformal Regression. CoRR abs/2308.11975 (2023) - [i2]Tuwe Löfström, Helena Löfström, Ulf Johansson, Cecilia Sönströd:
Calibrated Explanations for Regression. CoRR abs/2308.16245 (2023) - 2022
- [j10]Ulf Johansson, Cecilia Sönströd, Tuwe Löfström, Henrik Boström:
Rule extraction with guarantees from regression models. Pattern Recognit. 126: 108554 (2022) - [c93]Helena Löfström, Karl Hammar, Ulf Johansson:
A Meta Survey of Quality Evaluation Criteria in Explanation Methods. CAiSE Forum 2022: 55-63 - [c92]Ulf Johansson, Henrik Boström, Khuong An Nguyen, Zhiyuan Luo, Lars Carlsson:
Preface. COPA 2022: 1-3 - [c91]Tuwe Löfström, Artem Ryasik, Ulf Johansson:
Tutorial for using conformal prediction in KNIME. COPA 2022: 4-23 - [c90]Amr Alkhatib, Henrik Boström, Ulf Johansson:
Assessing Explanation Quality by Venn Prediction. COPA 2022: 42-54 - [c89]Ulf Johansson, Tuwe Löfström, Niclas Ståhl:
Well-Calibrated Rule Extractors. COPA 2022: 72-91 - [c88]Dirar Sweidan, Ulf Johansson, Anders Gidenstam, Beatrice Alenljung:
Predicting Customer Churn in Retailing. ICMLA 2022: 635-640 - [c87]Ulf Johansson, Erik Wilderoth, Arsalan Sattari:
How Analytics is Changing Ice Hockey. LINHAC 2022: 49-59 - [c86]Erik Wilderoth, Ulf Johansson, Arsalan Sattari:
Where not to lose the puck. LINHAC 2022: 109-117 - [e1]Ulf Johansson, Henrik Boström, Khuong An Nguyen, Zhiyuan Luo, Lars Carlsson:
Conformal and Probabilistic Prediction with Applications, 24-26 August 2022, Brighton, UK. Proceedings of Machine Learning Research 179, PMLR 2022 [contents] - [i1]Helena Löfström, Karl Hammar, Ulf Johansson:
A Meta Survey of Quality Evaluation Criteria in Explanation Methods. CoRR abs/2203.13929 (2022) - 2021
- [c85]Henrik Boström, Ulf Johansson, Tuwe Löfström:
Mondrian conformal predictive distributions. COPA 2021: 24-38 - [c84]Ulf Johansson, Tuwe Löfström, Henrik Boström:
Calibrating multi-class models. COPA 2021: 111-130 - [c83]Simon Arvidsson, Patrick Gabrielsson, Ulf Johansson:
Texture Mapping of Flags onto Polandball Characters using Convolutional Neural Nets. IJCNN 2021: 1-7 - [c82]Ulf Johansson, Tuwe Löfström, Henrik Boström:
Well-Calibrated and Sharp Interpretable Multi-Class Models. MDAI 2021: 193-204 - [c81]Arsalan Sattari, Ulf Johansson, Erik Wilderoth, Jasmin Jakupovic, Peter Larsson-Green:
The Interpretable Representation of Football Player Roles Based on Passing/Receiving Patterns. MLSA@PKDD/ECML 2021: 62-76 - [c80]Ulf Johansson, Henrik Boström, Tuwe Löfström:
Investigating Normalized Conformal Regressors. SSCI 2021: 1-8 - 2020
- [j9]Henrik Linusson, Ulf Johansson, Henrik Boström:
Efficient conformal predictor ensembles. Neurocomputing 397: 266-278 (2020) - [c79]Henrik Boström, Ulf Johansson:
Mondrian conformal regressors. COPA 2020: 114-133 - [c78]Ulf Johansson, Tuwe Löfström:
Well-calibrated and specialized probability estimation trees. SDM 2020: 415-423
2010 – 2019
- 2019
- [j8]Rubén Buendía, Thierry Kogej, Ola Engkvist, Lars Carlsson, Henrik Linusson, Ulf Johansson, Paolo Toccaceli, Ernst Ahlberg:
Accurate Hit Estimation for Iterative Screening Using Venn-ABERS Predictors. J. Chem. Inf. Model. 59(3): 1230-1237 (2019) - [j7]Ulf Johansson, Tuve Löfström, Henrik Linusson, Henrik Boström:
Efficient Venn predictors using random forests. Mach. Learn. 108(3): 535-550 (2019) - [c77]Chandadevi Giri, Ulf Johansson, Tuwe Löfström:
Predictive Modeling of Campaigns to Quantify Performance in Fashion Retail Industry. IEEE BigData 2019: 2267-2273 - [c76]Ulf Johansson, Tuwe Löfström, Henrik Boström, Cecilia Sönströd:
Interpretable and specialized conformal predictors. COPA 2019: 3-22 - [c75]Henrik Boström, Ulf Johansson, Anders Vesterberg:
Predicting with Confidence from Survival Data. COPA 2019: 123-141 - [c74]Ulf Johansson, Cecilia Sönströd, Tuwe Löfström, Henrik Boström:
Customized Interpretable Conformal Regressors. DSAA 2019: 221-230 - [c73]Ulf Johansson, Patrick Gabrielsson:
Are Traditional Neural Networks Well-Calibrated? IJCNN 2019: 1-8 - [c72]Ulf Johansson, Tuwe Löfström, Henrik Boström:
Calibrating Probability Estimation Trees using Venn-Abers Predictors. SDM 2019: 28-36 - 2018
- [j6]Ulf Johansson, Henrik Linusson, Tuve Löfström, Henrik Boström:
Interpretable regression trees using conformal prediction. Expert Syst. Appl. 97: 394-404 (2018) - [c71]Ulf Johansson, Tuwe Löfström, Håkan Sundell, Henrik Linusson, Anders Gidenstam, Henrik Boström:
Venn predictors for well-calibrated probability estimation trees. COPA 2018: 3-14 - [c70]Henrik Linusson, Ulf Johansson, Henrik Boström, Tuve Löfström:
Classification with Reject Option Using Conformal Prediction. PAKDD (1) 2018: 94-105 - 2017
- [j5]Henrik Boström, Henrik Linusson, Tuve Löfström, Ulf Johansson:
Accelerating difficulty estimation for conformal regression forests. Ann. Math. Artif. Intell. 81(1-2): 125-144 (2017) - [c69]Rikard König, Ulf Johansson, Maria Riveiro, Peter Brattberg:
Modeling Golf Player Skill Using Machine Learning. CD-MAKE 2017: 275-294 - [c68]Henrik Linusson, Ulf Norinder, Henrik Boström, Ulf Johansson, Tuve Löfström:
On the Calibration of Aggregated Conformal Predictors. COPA 2017: 154-173 - [c67]Ernst Ahlberg, Susanne Winiwarter, Henrik Boström, Henrik Linusson, Tuve Löfström, Ulf Norinder, Ulf Johansson, Ola Engkvist, Oscar Hammar, Claus Bendtsen, Lars Carlsson:
Using Conformal Prediction to Prioritize Compound Synthesis in Drug Discovery. COPA 2017: 174-184 - [c66]Ulf Johansson, Henrik Linusson, Tuve Löfström, Henrik Boström:
Model-agnostic nonconformity functions for conformal classification. IJCNN 2017: 2072-2079 - 2016
- [c65]Henrik Boström, Henrik Linusson, Tuve Löfström, Ulf Johansson:
Evaluation of a Variance-Based Nonconformity Measure for Regression Forests. COPA 2016: 75-89 - [c64]Henrik Linusson, Ulf Johansson, Henrik Boström, Tuve Löfström:
Reliable Confidence Predictions Using Conformal Prediction. PAKDD (1) 2016: 77-88 - 2015
- [j4]Tuve Löfström, Henrik Boström, Henrik Linusson, Ulf Johansson:
Bias reduction through conditional conformal prediction. Intell. Data Anal. 19(6): 1355-1375 (2015) - [c63]Maria Riveiro, Anders Dahlbom, Rikard König, Ulf Johansson, Peter Brattberg:
Supporting Golf Coaching and Swing Instruction with Computer-Based Training Systems. HCI (24) 2015: 279-290 - [c62]Rikard König, Ulf Johansson, Ann Lindqvist, Peter Brattberg:
Interesting Regression- and Model Trees Through Variable Restrictions. KDIR 2015: 281-292 - [c61]Ulf Johansson, Cecilia Sönströd, Henrik Linusson:
Efficient conformal regressors using bagged neural nets. IJCNN 2015: 1-8 - [c60]Lars Carlsson, Ernst Ahlberg, Henrik Boström, Ulf Johansson, Henrik Linusson:
Modifications to p-Values of Conformal Predictors. SLDS 2015: 251-259 - [c59]Ulf Johansson, Ernst Ahlberg, Henrik Boström, Lars Carlsson, Henrik Linusson, Cecilia Sönströd:
Handling Small Calibration Sets in Mondrian Inductive Conformal Regressors. SLDS 2015: 271-280 - [c58]Patrick Gabrielsson, Ulf Johansson:
High-Frequency Equity Index Futures Trading Using Recurrent Reinforcement Learning with Candlesticks. SSCI 2015: 734-741 - 2014
- [j3]Ulf Johansson, Henrik Boström, Tuve Löfström, Henrik Linusson:
Regression conformal prediction with random forests. Mach. Learn. 97(1-2): 155-176 (2014) - [c57]Ulf Johansson, Cecilia Sönströd, Henrik Linusson, Henrik Boström:
Regression trees for streaming data with local performance guarantees. IEEE BigData 2014: 461-470 - [c56]Ulf Johansson, Cecilia Sönströd, Rikard König:
Accurate and interpretable regression trees using oracle coaching. CIDM 2014: 194-201 - [c55]Rikard König, Ulf Johansson:
Rule extraction using genetic programming for accurate sales forecasting. CIDM 2014: 210-216 - [c54]Patrick Gabrielsson, Ulf Johansson, Rikard König:
Co-evolving online high-frequency trading strategies using grammatical evolution. CIFEr 2014: 473-480 - [c53]Henrik Linusson, Ulf Johansson, Henrik Boström, Tuve Löfström:
Efficiency Comparison of Unstable Transductive and Inductive Conformal Classifiers. AIAI Workshops 2014: 261-270 - [c52]Ulf Johansson, Rikard König, Henrik Linusson, Tuve Löfström, Henrik Boström:
Rule Extraction with Guaranteed Fidelity. AIAI Workshops 2014: 281-290 - [c51]Henrik Linusson, Ulf Johansson, Tuve Löfström:
Signed-Error Conformal Regression. PAKDD (1) 2014: 224-236 - 2013
- [c50]Ulf Johansson, Rikard König, Tuve Löfström, Henrik Boström:
Evolved decision trees as conformal predictors. IEEE Congress on Evolutionary Computation 2013: 1794-1801 - [c49]Ulf Johansson, Tuve Löfström, Henrik Boström:
Overproduce-and-select: The grim reality. CIEL 2013: 52-59 - [c48]Patrick Gabrielsson, Rikard König, Ulf Johansson:
Evolving Hierarchical Temporal Memory-Based Trading Models. EvoApplications 2013: 213-222 - [c47]Ulf Johansson, Henrik Boström, Tuve Löfström:
Conformal Prediction Using Decision Trees. ICDM 2013: 330-339 - [c46]Ulf Johansson, Tuve Löfström, Henrik Boström:
Random brains. IJCNN 2013: 1-8 - [c45]Tuve Löfström, Ulf Johansson, Henrik Boström:
Effective utilization of data in inductive conformal prediction using ensembles of neural networks. IJCNN 2013: 1-8 - 2012
- [j2]Ulf Johansson, Cecilia Sönströd, Tuve Löfström, Henrik Boström:
Obtaining accurate and comprehensible classifiers using oracle coaching. Intell. Data Anal. 16(2): 247-263 (2012) - [c44]Patrick Gabrielsson, Rikard König, Ulf Johansson:
Hierarchical Temporal Memory-based algorithmic trading of financial markets. CIFEr 2012: 1-8 - [c43]Ulf Johansson, Tuve Löfström:
Producing implicit diversity in ANN ensembles. IJCNN 2012: 1-8 - 2011
- [c42]Cecilia Sönströd, Ulf Johansson, Rikard König:
Evolving accurate and comprehensible classification rules. IEEE Congress on Evolutionary Computation 2011: 1436-1443 - [c41]Ulf Johansson, Cecilia Sönströd, Tuve Löfström:
One tree to explain them all. IEEE Congress on Evolutionary Computation 2011: 1444-1451 - [c40]Ulf Johansson, Tuve Löfström, Cecilia Sönströd:
Locally induced predictive models. SMC 2011: 1735-1740 - 2010
- [c39]Rikard König, Ulf Johansson, Tuve Löfström, Lars Niklasson:
Improving GP classification performance by injection of decision trees. IEEE Congress on Evolutionary Computation 2010: 1-8 - [c38]Ulf Johansson, Rikard König, Tuve Löfström, Lars Niklasson:
Using Imaginary Ensembles to Select GP Classifiers. EuroGP 2010: 278-288 - [c37]Ulf Johansson, Rikard König, Lars Niklasson:
Genetic rule extraction optimizing brier score. GECCO 2010: 1007-1014 - [c36]Ulf Johansson, Cecilia Sönströd, Tuve Löfström:
Oracle Coached Decision Trees and Lists. IDA 2010: 67-78 - [c35]Tuve Löfström, Ulf Johansson, Henrik Boström:
Comparing methods for generating diverse ensembles of artificial neural networks. IJCNN 2010: 1-6 - [c34]Cecilia Sönströd, Ulf Johansson, Henrik Boström, Ulf Norinder:
Pin-pointing concept descriptions. SMC 2010: 2956-2963 - [p2]Ulf Johansson, Rikard König, Lars Niklasson:
Genetically Evolved kNN Ensembles. Data Mining 2010: 299-313
2000 – 2009
- 2009
- [c33]Ulf Johansson, Cecilia Sönströd, Tuve Löfström, Rikard König:
Using genetic programming to obtain implicit diversity. IEEE Congress on Evolutionary Computation 2009: 2454-2459 - [c32]Ulf Johansson, Lars Niklasson:
Evolving decision trees using oracle guides. CIDM 2009: 238-244 - [c31]Tuve Löfström, Ulf Johansson, Henrik Boström:
Ensemble member selection using multi-objective optimization. CIDM 2009: 245-251 - [c30]Ulf Johansson, Cecilia Sönströd:
Fish or Shark - Data Mining Online Poker. DMIN 2009: 97-103 - [c29]Cecilia Sönströd, Ulf Johansson, Tuve Löfström:
Evaluating Algorithms for Concept Description. DMIN 2009: 354-360 - [p1]Ulf Johansson, Rikard König, Tuve Löfström, Cecilia Sönströd, Lars Niklasson:
Post-processing Evolved Decision Trees. Foundations of Computational Intelligence (4) 2009: 149-164 - 2008
- [c28]Ulf Johansson, Rikard König, Tuve Löfström, Lars Niklasson:
Increasing rule extraction accuracy by post-processing GP trees. IEEE Congress on Evolutionary Computation 2008: 3005-3010 - [c27]Ulf Johansson, Rikard König, Lars Niklasson:
Evolving a Locally Optimized Instance Based Learner. DMIN 2008: 124-129 - [c26]Cecilia Sönströd, Ulf Johansson, Rikard König, Lars Niklasson:
Genetic Decision Lists for Concept Description. DMIN 2008: 450-456 - [c25]Ulf Johansson, Henrik Boström, Rikard König:
Extending Nearest Neighbor Classification with Spheres of Confidence. FLAIRS 2008: 282-287 - [c24]Rikard König, Ulf Johansson, Lars Niklasson:
Using Genetic Programming to Increase Rule Quality. FLAIRS 2008: 288-293 - [c23]Rikard König, Ulf Johansson, Lars Niklasson:
G-REX: A Versatile Framework for Evolutionary Data Mining. ICDM Workshops 2008: 971-974 - [c22]Tuve Löfström, Ulf Johansson, Henrik Boström:
On the Use of Accuracy and Diversity Measures for Evaluating and Selecting Ensembles of Classifiers. ICMLA 2008: 127-132 - [c21]Cecilia Sönströd, Ulf Johansson, Ulf Norinder, Henrik Boström:
Comprehensible Models for Predicting Molecular Interaction with Heart-Regulating Genes. ICMLA 2008: 559-564 - [c20]Ulf Johansson, Tuve Löfström, Henrik Boström:
The problem with ranking ensembles based on training or validation performance. IJCNN 2008: 3222-3228 - [c19]Ulf Johansson, Tuve Löfström, Lars Niklasson:
Evaluating Standard Techniques for Implicit Diversity. PAKDD 2008: 592-599 - [c18]Ulf Johansson, Cecilia Sönströd, Tuve Löfström, Henrik Boström:
Chipper - A Novel Algorithm for Concept Description. SCAI 2008: 133-140 - 2007
- [b1]Ulf Johansson:
Obtaining Accurate and Comprehensible Data Mining Models: An Evolutionary Approach. Linköping University, Sweden, 2007 - [c17]Rikard König, Ulf Johansson, Lars Niklasson:
Genetic programming - a tool for flexible rule extraction. IEEE Congress on Evolutionary Computation 2007: 1304-1310 - [c16]Cecilia Sönströd, Ulf Johansson, Rikard König:
Towards a Unified View on Concept Description. DMIN 2007: 59-65 - [c15]Rikard König, Ulf Johansson, Lars Niklasson:
Instance Ranking using Ensemble Spread. DMIN 2007: 73-78 - [c14]Tuve Löfström, Ulf Johansson, Lars Niklasson:
Empirically investigating the importance of diversity. FUSION 2007: 1-8 - [c13]Ulf Johansson, Tuve Löfström, Lars Niklasson:
The Importance of Diversity in Neural Network Ensembles - An Empirical Investigation. IJCNN 2007: 661-666 - [c12]Ulf Johansson, Rikard König, Lars Niklasson:
Inconsistency - Friend or Foe. IJCNN 2007: 1383-1388 - [c11]Cecilia Sönströd, Ulf Johansson:
Concept Description - A Fresh Look. IJCNN 2007: 2415-2420 - 2006
- [c10]Ulf Johansson, Tuve Löfström, Rikard König, Lars Niklasson:
Introducing GEMS - A Novel Technique for Ensemble Creation. FLAIRS 2006: 700-705 - [c9]Tuve Löfström, Rikard König, Ulf Johansson, Lars Niklasson, Mattias Strand, Tom Ziemke:
Benefits of relating the Retail Domain and Information Fusion. FUSION 2006: 1-4 - [c8]Ulf Johansson, Tuve Löfström, Rikard König, Lars Niklasson:
Genetically Evolved Trees Representing Ensembles. ICAISC 2006: 613-622 - [c7]Ulf Johansson, Tuve Löfström, Rikard König, Cecilia Sönströd, Lars Niklasson:
Rule Extraction from Opaque Models-- A Slightly Different Perspective. ICMLA 2006: 22-27 - [c6]Ulf Johansson, Cecilia Sönströd, Lars Niklasson:
Explaining Winning Poker--A Data Mining Approach. ICMLA 2006: 129-134 - [c5]Ulf Johansson, Tuve Löfström, Rikard König, Lars Niklasson:
Building Neural Network Ensembles using Genetic Programming. IJCNN 2006: 1260-1265 - 2005
- [c4]Ulf Johansson, Tuve Löfström, Lars Niklasson:
Obtaining Accurate Neural Network Ensembles. CIMCA/IAWTIC 2005: 103-108 - 2004
- [c3]Ulf Johansson, Rikard König, Lars Niklasson:
The Truth is In There - Rule Extraction from Opaque Models Using Genetic Programming. FLAIRS 2004: 658-663 - [c2]Ulf Johansson, Cecilia Sönströd, Lars Niklasson:
Why rule extraction matters. IASTED Conf. on Software Engineering and Applications 2004: 47-52 - [c1]Tuve Löfström, Ulf Johansson, Lars Niklasson:
Rule Extraction by Seeing Through the Model. ICONIP 2004: 555-560 - 2003
- [j1]Beverly A. Wagner, Ian Fillis, Ulf Johansson:
An empirical investigation into e-business adoption in the Scottish smaller firm. Int. J. Electron. Bus. 1(4): 408-422 (2003)
Coauthor Index
Tuve Löfström
aka: Tuwe Löfström
aka: Tuwe Löfström
[j11] [c96] [c95] [c94] [i5] [i4] [i2] [j10] [c91] [c89] [c85] [c84] [c82] [c80] [c78] [j7] [c77] [c76] [c74] [c72] [j6] [c71] [c70] [j5] [c68] [c67] [c66] [c65] [c64] [j4] [j3] [c53] [c52] [c51] [c50] [c49] [c47] [c46] [c45] [j2] [c43] [c41] [c40] [c39] [c38] [c36] [c35] [c33] [c31] [c29] [p1] [c28] [c22] [c20] [c19] [c18] [c14] [c13] [c10] [c9] [c8] [c7] [c5] [c4] [c1]