Tony Lindgren's Publications

Tony Lindgren's publications

2023:

K. Sun, S. Magnússon, O. Steinert, T. Lindgren, "Robust Contrastive Learning and Multi-shot Voting for High-dimensional Multivariate Data-driven Prognostics", 2023 IEEE International Conference on Prognostics and Health Management (ICPHM), (2023) Link

Bull, L. A., Di Francesco, D., Dhada, M., Steinert, O., Lindgren, T., Parlikad, A. K., Duncan, A. B., & Girolami, M., "Hierarchical Bayesian modeling for knowledge transfer across engineering fleets via multitask learning",
Computer-Aided Civil and Infrastructure Engineering, 00, pp. 1–28, (2023) Link

Dhada, M., Parlikad, A.K., Steinert, O. and Lindgren, T., "Weibull recurrent neural networks for failure prognosis using histogram data",
Neural Computing and Applications, (2023) Link

Z. Kharazian, M. Rahat, F. Gama, P. S. Mashhadi, S. Nowaczyk, T. Lindgren, S. Magnússon, "AID4HAI: Automatic Idea Detection for Healthcare-Associated Infections from Twitter, a Framework Based on Active Learning and Transfer Learning",
International Symposium on Intelligent Data Analysis, (2023) Link

2022:

Kuratomi H. A., Miliou I., Lee Z., Lindgren T. & Papapetrou P., "JUICE: JUstIfied Counterfactual Explanations",
In Proceedings of Discovery Science. DS , (2022) Link

Kuratomi, A., Pitoura, E., Papapetrou, P., Lindgren, T. and Tsaparas, Panayioti, "Measuring the Burden of (Un)fairness Using Counterfactuals",
Machine Learning and Principles and Practice of Knowledge Discovery in Databases, pp. 402–417, (2022) Link

T. Lindgren and O. Steinert, "Low dimensional synthetic data generation for improving data driven prognostic models",
In IEEE International Conference on Prognostics and Health Management (ICPHM 2022) (2022), pp. 173–182. Link

2021:

T. Lindgren, "Hybrid feature tweaking: Combining random forest similarity tweaking with CLPFD",
In 2021 7th International Conference on Computing and Data Engineering (2021), pp. 20–26. Link

A. Kuratomi, T. Lindgren, and P. Papapetrou, "Prediction of Global Navigation Satellite System Positioning Errors with Guarantees",
Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track (2021), pp. 562–578. Link

L. Zed, A. Nicholas, P. Papapetrou, and T. Lindgren, ”Z-Hist: A Temporal Abstraction of Multivariate Histogram Snapshot”,
In Proceedings of the Advances in Intelligent Data Analysis XIX (IDA) , Springer Internationl Publishing, pp. 376–388, 2021. Link

2020:

L. Zed, T. Lindgren, and P. Papapetrou, "Z-Miner: an efficient method for mining frequent arrangements of event intervals",
KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2020), pp. 524–534. Link

M. Mammo and T. Lindgren, ”Evaluation of Dimensionality Reduction Techniques-Principal Feature Analysis in case of Text Classification Problems”,
In Proceedings of the 6th International Conference on Computing and Data Engineering (ICCDE) (2020), pp. 75–79. Link

2019:

G. D. Ranasinghe, T. Lindgren, M. Girolami and A. K. Parlikad, "A Methodology for Prognostics Under the Conditions of Limited Failure Data Availability",
IEEE Access vol. 7, pp. 183996-184007, 2019. Link

T. Lindgren, P. Papapetrou, I. Samsten and L. Asker, "Example-Based Feature Tweaking Using Random Forests",
2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI) (2019), pp. 53-60. Link

2018:

H. Boström, R. Gurung, T. Lindgren, and U. Johansson, "Explaining Random Forest Predictions with Association Rules",
Archives of Data Science, Series A, vol. 5, no. 1, pp. 121-130, 2018. Link

T. Lindgren, "On Data Driven Organizations and the Necessity of Interpretable Models",
Smart Grid and Internet of Things, Second EAI International Conference, (SGIoT) (2018), pp. 121-130. Link

T. Lindgren, "Random Rule Sets – Combining Random Covering with the Random Subspace Method",
International Journal of Machine Learning and Computing vol. 8, no. 1, pp. 8-13, 2018. Pdf

R. Gurung, T. Lindgren, and H. Boström, "Learning Random Forest from Histogram Data Using Split Specific Axis Rotation",
International Journal of Machine Learning and Computing vol. 8, no. 1, pp. 74-79, 2018. Pdf

2017:

H. Boström, L. Asker, R. Gurung, I. Karlsson, T. Lindgren and P. Papapetrou, "Conformal Prediction Using Random Survival Forests",
16th IEEE International Conference on Machine Learning and Applications (ICMLA) (2017), pp. 812-817. Link

R. Gurung, T. Lindgren and H. Boström, "Predicting NOx sensor failure in heavy duty trucks using histogram-based random forests",
International Journal of Prognostics and Health Management (2017) Link

T. Lindgren, "Randomized Separate and Conquer Rule induction",
Proceedings of the International Conference on Compute and Data Analysis (ICCDA) (2017) Link

J. Biteus, and T. Lindgren, "Planning Flexible Maintenance for Heavy Trucks using Machine Learning Models, Constraint Programming, and Route Optimization,"
SAE Int. J. Mater. Manf. 10(3):2017, doi:10.4271/2017-01-0237. (2017) Link

2016:

K. Hansson, A. Talantsev, J. Nouri, L. Ekenberg and T. Lindgren, "Open government ideologies in post-soviet countries",
International Journal of Electronic Governance, vol. 8, no. 3, pp. 244-264 (2016) Link

T. Lindgren, "Indexing rules in rule sets for fast classification",
Proceedings of the International Conference on Artificial Intelligence and Robotics and the International Conference on Automation, Control and Robotics Engineering (ICAIR-CACRE) (2016) Pdf

R. Gurung, T. Lindgren and H. Boström, "Learning Decision Trees from Histogram Data Using Multiple Subsets of Bins",
In Proceedings of the Twenty-Ninth International Florida Artificial Intelligence Research Society Conference (FLAIRS) (2016), pp. 430-435 Pdf

2015:

T. Lindgren, "Model Based Sampling - Fitting an ensemble of models into a single model",
In Procs. of 2015 International conference on Computational Science and Computational Intellingence (CSCI) (2015), pp. 186-191 Pdf

R. Gurung, T. Lindgren and H. Boström, "Learning Decision Trees From Histogram Data",
In Procs. of the 11th International Conference on Data Mining (DMIN) (2015), pp. 139-145 Pdf

2014:

T. Lindgren and J. Biteus, "Expert Guided Adaptive Maintenance",
In Procs. of the 2nd European Conference of the Prognostics and Health Management Society (PHME) (2014), pp. 815-820 Pdf

2013:

T. Lindgren, H. Warnquist and M. Eineborg, "Improving the Maintenance Planning of Heavy Trucks using Constraint Programming",
In Procs. of the 12th International Workshop on Constraint Modelling and Reformulation
Co-located with the 19th International Conference on Principles and Practice of Constraint Programming (MODREF) (2013), pp. 74-90 Pdf

2012:

T. Lindgren, "Troubleshooting ECU programmed by Bodybuilders",
Proc. of the 1st International Conference on Connected Vehicles and Expo (2012) Pdf

2006:

T. Lindgren, "On Handling Conflicts between Rules with Numerical Features",
Proc. of the 2006 ACM Symposium on Applied Computing (2006), pp. 37-41 PostScript

2004:

T. Lindgren, "Methods for Rule Conflict Resolution",
Proc. of the 15th European Conference on Machine Learning (2004), pp. 262-273 PostScript

T. Lindgren and H. Boström, "Resolving Rule Conflicts with Double Induction",
Journal of Intelligent Data Analysis (2004), Volume 8 Issue 5, pp. 457-468
2003:

T. Lindgren and H. Boström, "Resolving Rule Conflicts with Double Induction",
Proc. of the 5th International Symposium on Intelligent Data Analysis (IDA) (2003), pp. 60-67 PostScript

2002:

T. Lindgren and H. Boström, "Classification with Intersecting Rules",
Proc. of the 13th International Conference on Algorithmic Learning Theory (ALT) (2002), pp. 395-402 PostScript

2000:

T. Lindgren, "Anytime Inductive Logic Programming",
Proc. of the 15th International Conference on Computers And Their Applications (2000), pp. 439-442 PostScript

1999:

T. Lindgren, "Anytime Inductive Logic Programming",
Master Thesis, DSV, Stockholm University, Sweden, Sep. 1999 PostScript

Last modified: Thu May 16 09:29:51 MET DST 2021