| Planning flexible maintenance for heavy trucks using machine learning models, constraint programming, and route optimization J Biteus, T Lindgren SAE International Journal of Materials and Manufacturing 10 (3), 306-315, 2017 | 30 | 2017 |
| Methods for rule conflict resolution T Lindgren Machine Learning: ECML 2004: 15th European Conference on Machine Learning …, 2004 | 27 | 2004 |
| Resolving rule conflicts with double induction T Lindgren, H Boström Intelligent Data Analysis 8 (5), 457-468, 2004 | 17 | 2004 |
| Conformal prediction using random survival forests H Bostrom, L Asker, R Gurung, I Karlsson, T Lindgren, P Papapetrou 2017 16th IEEE International Conference on Machine Learning and Applications …, 2017 | 13* | 2017 |
| Learning decision trees from histogram data using multiple subsets of bins RB Gurung, T Lindgren, H Boström Twenty-Ninth International Florida Artificial Intelligence Research Society …, 2016 | 13 | 2016 |
| Resolving rule conflicts with double induction T Lindgren, H Boström IDA, 60-67, 2003 | 13 | 2003 |
| Classification with intersecting rules T Lindgren, H Boström ALT, 395-402, 2002 | 13 | 2002 |
| A methodology for prognostics under the conditions of limited failure data availability GD Ranasinghe, T Lindgren, M Girolami, AK Parlikad IEEE Access 7, 183996-184007, 2019 | 10 | 2019 |
| Z-miner: an efficient method for mining frequent arrangements of event intervals Z Lee, T Lindgren, P Papapetrou Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020 | 9 | 2020 |
| Predicting NOx sensor failure in heavy duty trucks using histogram-based random forests RB Gurung, T Lindgren, H Bostr International Journal of Prognostics and Health Management 8 (1), 2017 | 8 | 2017 |
| Learning decision trees from histogram data RB Gurung, T Lindgren, H Boström 11th International Conference on Data Mining (DMIN'15), Las Vegas, Nevada …, 2015 | 8 | 2015 |
| Learning random forest from histogram data using split specific axis rotation RB Gurung, T Lindgren, H Boström International Journal of Machine Learning and Computing 8 (1), 74-79, 2018 | 7 | 2018 |
| Explaining random forest predictions with association rules H Boström, RB Gurung, T Lindgren, U Johansson Archives of Data Science, Series A (Online First) 5 (1), A05, 2018 | 7 | 2018 |
| Open government ideologies in post-soviet countries K Hansson, A Talantsev, J Nouri, L Ekenberg, T Lindgren International Journal of Electronic Governance 8 (3), 244-264, 2016 | 7 | 2016 |
| Anytime inductive logic programming. T Lindgren CATA, 439-442, 2000 | 6 | 2000 |
| Example-based feature tweaking using random forests T Lindgren, P Papapetrou, I Samsten, L Asker 2019 IEEE 20th International Conference on Information Reuse and Integration …, 2019 | 5 | 2019 |
| APS failure at Scania trucks data set T Lindgren, J Biteus | 5 | 2016 |
| Model Based Sampling-Fitting an Ensemble of Models into a Single Model T Lindgren 2015 International Conference on Computational Science and Computational …, 2015 | 5 | 2015 |
| On handling conflicts between rules with numerical features T Lindgren Proceedings of the 2006 ACM symposium on Applied computing, 37-41, 2006 | 5 | 2006 |
| On data driven organizations and the necessity of interpretable models T Lindgren Smart Grid and Internet of Things: Second EAI International Conference …, 2019 | 4 | 2019 |
Henrik BoströmProfessor in computer science - data science systems, KTH Royal Institute of TechnologyVerifierad e-postadress på kth.se
