| 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 | 42 | 2017 |
| Methods for rule conflict resolution T Lindgren European Conference on Machine Learning, 262-273, 2004 | 32 | 2004 |
| Hierarchical Bayesian modeling for knowledge transfer across engineering fleets via multitask learning LA Bull, D Di Francesco, M Dhada, O Steinert, T Lindgren, AK Parlikad, ... Computer‐Aided Civil and Infrastructure Engineering 38 (7), 821-848, 2023 | 30* | 2023 |
| 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 | 21 | 2020 |
| Resolving rule conflicts with double induction T Lindgren, H Boström Intelligent Data Analysis 8 (5), 457-468, 2004 | 17 | 2004 |
| 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 | 15 | 2018 |
| 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 | 14 | 2019 |
| 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 | 14* | 2017 |
| Resolving rule conflicts with double induction T Lindgren, H Boström International Symposium on Intelligent Data Analysis, 60-67, 2003 | 14 | 2003 |
| Weibull recurrent neural networks for failure prognosis using histogram data M Dhada, AK Parlikad, O Steinert, T Lindgren Neural Computing and Applications 35 (4), 3011-3024, 2023 | 13 | 2023 |
| Learning decision trees from histogram data using multiple subsets of bins RB Gurung, T Lindgren, H Boström The Twenty-Ninth International Flairs Conference, 2016 | 13 | 2016 |
| Classification with intersecting rules T Lindgren, H Boström Algorithmic Learning Theory: 13th International Conference, ALT 2002 Lübeck …, 2002 | 13 | 2002 |
| 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 | 12 | 2017 |
| APS failure at Scania trucks data set T Lindgren, J Biteus UCI Machine Learning Repository: APS Failure at Scania Trucks Data Set …, 2016 | 11* | 2016 |
| 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 | 9 | 2016 |
| SCANIA component X dataset: a real-world multivariate time series dataset for predictive maintenance Z Kharazian, T Lindgren, S Magnússon, O Steinert, OA Reyna arXiv preprint arXiv:2401.15199, 2024 | 8 | 2024 |
| Z-Time: efficient and effective interpretable multivariate time series classification Z Lee, T Lindgren, P Papapetrou Data mining and knowledge discovery 38 (1), 206-236, 2024 | 8 | 2024 |
| 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 |
| Anytime inductive logic programming. T Lindgren CATA, 439-442, 2000 | 8 | 2000 |
| 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 |
Henrik BoströmProfessor of computer science - data science systems, KTH Royal Institute of TechnologyVerified email at kth.se
