About me

I am a Full Professor of Artificial Intelligence at the Department of Computer Science of the Vrije Universiteit Amsterdam and chair of the Quantitative Data Analytics group.

My research focuses on machine learning and its applications, the latter primarily applied in the domain of health and wellbeing. The research ranges from more fundamental machine learning research (including efficiency, explainability, safety, incorporating domain knowledge, and hybrid intelligence) to application driven research focusing on for instance predictive modeling for diseases, personalized therapies and support systems, and eHealth and mHealth systems. My inaugural speech provides more detail, you can find it here.

I am involved in a variety of research projects and teach several courses related to Artificial Intelligence and Machine Learning. In addition, I am the chair of the Examination Board (for the subcommittee for the Department of Computer Science) of the Faculty of Science.

Next to my academic work, I have also founded the company PersonalAIze together with three colleagues to bring academic insights in the area of Machine Learning into practice.

Before starting in the Quantitative Data Analytics group, I have been an Associate Professor in the Computational Intelligence group. I have also been a Visiting Scientist within the Clinical Decision Making Group headed by Peter Szolovits at MIT (CSAIL) during the Summer of 2015 and a PostDoc at the Department of Computer Science and Engineering at the University of Minnesota in the group of Maria Gini (Fall 2007). Before 2012 I was part of the Agent Systems Group at the VU. I obtained my PhD degree in 2007 from the Vrije Universiteit Amsterdam as well.

Quantitative Data Analytics team

Current Team

Frank Bennis (PostDoc)
Yvonne Blokland (Coordinator AI and Health VU)
Tariq Dam (PhD student)
Lucas Fleuren (PhD student)
Anne Fischer (PhD student)
Vincent Francois Lavet (Assistant Professor)
Arwin Gansekoele (PhD student)
Joshua Jaeger (PhD student)
Floris den Hengst (PhD student)

Current Team (cont.)

Jacob Kooi (PhD student)
Olivier Moulin (PhD student)
William Nkhono (PhD student)
Martijn Otten (PhD student)
Leonardos Pantiskas (PhD student)
Luca Roggeveen (PhD student)
Louk Smalbil (PhD student)
David Wilson Romero Guzman (PhD student)
Luis Silvestrin (PhD student)
Shujian Yu (Assistant Professor)

Former

Lucas Fleuren (PhD received 16-02-2023)
Jan Klein (PhD received 07-09-2023)
Thilo Reich
Ali el Hassouni (PhD received 18-01-2022)
Eoin Grua (PhD received 03-12-2021)
Steffen Völker
Ward van Breda (PhD received 23-06-2020)
Amin Tabatabaei
Bart Kamphorst
Giorgos Karafotias (PhD received 24-02-2016)
Robbert-Jan Merk (PhD received 06-02-2013)
Fiemke Both (PhD received 05-06-2012)
Rianne van Lambalgen (PhD received 03-04-2012)
Muhammad Umair (PhD received 06-02-2012)
Syed Waqar Jaffry (PhD received 09-09-2011)
Research Projects
  • Machine learning for intensive care (Amsterdam UMC) - improving treatments at the ICU using reinforcement learning
  • Machine learning for the safety domain (Ministry of the Interior) - using machine learning techniques in the safety domain
  • Constrained reinforcement learning (ING) - Constrained reinforcement learning for personalization in highly regulated domains (ING)
  • RESCUED (CVON) - predictive modeling of sudden cardia arrest using machine learning
  • Efficient Deep Learning (NWO) - fundamental improvements to deep learning methods
  • COCOON (HealthHolland) - prediction of pre-term birth with a variety of data sources
  • IMPALA (EDCTP) - development of innovative sensors with machine learning algorithms timely detect and predict critical illness in low resource settings
  • ICARE4OLD (H2020) - develop high quality decision support for better prognostication of health trajectories of elderly using machine learning
  • Stress in Action (NWO) - understanding real life stress using state-of-the-art measurement devices and machine learning
Teaching

I teach several courses. Information regarding the courses I teach can be found on Canvas. I'm currently involved in teaching/coordinating the following courses:

  • Data Mining Techniques
  • Machine Learning for the Quantified Self
  • Bedrijfscase
  • Mini Master Project (coordinator)


Committees/Network Organizations

Contact details

Address

Vrije Universiteit Amsterdam
Faculty of Science
Department of Computer Science
De Boelelaan 1111, (NU VU, room 10A87)
1081 HV Amsterdam
The Netherlands

Phone

+31 20 5987772





Recent publications



2022



Journal publications

Bennis, F. C., Hoogendoorn, M., Aussems, C., and Korevaar, J. C., Prediction of heart failure 1 year before diagnosis in general practitioner patients using machine learning algorithms: a retrospective case–control study. BMJ open, 12(8), 2022.

van Boven, M. R., Henke, C. E., Leemhuis, A. G., Hoogendoorn, M., van Kaam, A. H., Königs, M., and Oosterlaan, J., Machine Learning Prediction Models for Neurodevelopmental Outcome After Preterm Birth: A Scoping Review and New Machine Learning Evaluation Framework. Pediatrics, 2022.

Dam, T. A., Roggeveen, L. F., van Diggelen, F., Fleuren, L. M., Jagesar, A. R., Otten, M., ..., Hoogendoorn, M., ..., and Elbers, P. W., Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learning. Annals of Intensive Care, 12(1), 2022, pp. 1-9.

Grua, E. M., De Sanctis, M., Malavolta, I., Hoogendoorn, M., and Lago, P., An evaluation of the effectiveness of personalization and self-adaptation for e-Health apps. Information and Software Technology, vol. 146, Elsevier, 2022.

den Hengst, F., François-Lavet, V., Hoogendoorn, M. and Harmelen, F. van, Planning for potential: efficient safe reinforcement learning. Machine Learning, Spinger, 2022, pp. 1-20.

Janssen, A., Hoogendoorn, M., Cnossen, M. H., Mathôt, R. A., OPTI‐CLOT Study Group and SYMPHONY Consortium, Application of SHAP values for inferring the optimal functional form of covariates in pharmacokinetic modeling. CPT: Pharmacometrics & Systems Pharmacology, 11(8), 2022, pp. 1100-1110.

Klein, J., Bhulai, S., Hoogendoorn, M., and van der Mei, R., Jasmine: A new Active Learning approach to combat cybercrime. Machine Learning with Applications, Elsevier, 2022.

Kirtley, O.J., Mens K. van, Hoogendoorn, M., Kapur, N., and Beurs, D. de, Translating promise into practice: A review of machine learning in suicide research and prevention, The Lancet Psychiatry, vol. 9(3), pp. 243-252, 2022.

van de Ven, P., Rollman, B. L., Hoogendoorn, M., and Kay-Lambkin, F., Storm clouds and silver linings: how digital technologies have helped us weather the Covid pandemic. Procedia Computer Science, 206, 2022, pp. 1-5.



Conference publications

den Hengst, F., François-Lavet, V., Hoogendoorn, M., and Harmelen, F. van, Reinforcement learning with option machines. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, International Joint Conferences on Artificial Intelligence Organization, 2022, pp. 2909-2915.

Moulin, O., Francois-Lavet, V., Elbers, P., and Hoogendoorn, M., Improving adaptability to new environments and removing catastrophic forgetting in Reinforcement Learning by using an eco-system of agents, In: Proceedings of the 21st IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, IEEE, 2022 (best student paper award).

Pantiskas, L., Verstoep, K., Hoogendoorn, M., and Bal, H., Taking ROCKET on an efficiency mission: Multivariate time series classification with LightWaves, 21st IEEE Int. Conf. on Machine Learning and Applications (IEEE ICMLA 2022), 2022.

Romero, D.W., Kuzina, A., Bekkers, E.J., Tomczak, J.M., and Hoogendoorn, M., CKConv: Continuous Kernel Convolution For Sequential Data, In: Proceedings of the International Conference on Learning Representations (ICLR), 2022.

Romero, D.W, Bruintjes, R.J., Tomczak, J.M., Bekkers, E.J., Hoogendoorn, M., and Gemert, J.C. van, FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes, In: Proceedings of the International Conference on Learning Representations (ICLR), 2022.



2021



Journal publications

Dam, T. A. et al., Some Patients Are More Equal Than Others: Variation in Ventilator Settings for Coronavirus Disease 2019 Acute Respiratory Distress Syndrome. Critical care explorations, 3(10), 2021.

Dongen, L.H. van, Harms, P.P., Hoogendoorn, M., Zimmerman, D.S., Lodder, E.M., ’t Hart, L.M., Herings, R., Weert, H.C.P.M. van, Nijpels, G., Swart, K.M.A., Heijden, A.A. van der, Blom, M.T., Elders, P.J., and Tan, H.L., Discovery of predictors of sudden cardiac arrest in diabetes: rationale and outline of the RESCUED (REcognition of Sudden Cardiac arrest vUlnErability in Diabetes) project, Open Heart, to appear, 2021.

Fleuren, L.M. et al., Risk factors for adverse outcomes during mechanical ventilation of 1152 COVID-19 patients: a multicenter machine learning study with highly granular data from the Dutch Data Warehouse, Intensive care medicine experimental 9.1 (2021): pp. 1-15.

Fleuren, L. M., et al., The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients. Critical Care, 25(1), 1-12, 2021.

Fleuren, L. M. et al.. Predictors for extubation failure in COVID-19 patients using a machine learning approach. Critical Care, 25(1), 1-10, 2021.

Gosselt, H.R., Verhoeven, M.M.A., Bulatović-Ćalasan, M., Welsing, P.M., de Rotte, M.C.F.J., Hazes, J.M.W., Lafeber, F.P.J.G., Hoogendoorn, M., and de Jonge, R.Complex Machine-Learning Algorithms and Multivariable Logistic Regression on Par in the Prediction of Insufficient Clinical Response to Methotrexate in Rheumatoid Arthritis, J. Pers. Med. 2021, 11(1), 44

Jiang, J., Kong, Q., Plumbley, M.D., Gilbert, N., Hoogendoorn, M., and Roijers, D.M., Deep Learning-Based Energy Disaggregation and On/Off Detection of Household Appliances, ACM Transactions on Knowledge Discovery from Data, 2021, vol. 15, pp. 1-21.

Roggeveen, L., el Hassouni, A., Ahrendt, J., Guo, T., MS, Fleuren, L.M., Thoral, P., Girbes, A.R.J., Hoogendoorn, M., and Elbers, P.W.G., Transatlantic transferability of a new reinforcement learning model for optimizing haemodynamic treatment for critically ill patients with sepsis, AI in Medicine, 2021.

Thoral, P. J. et al., Explainable Machine Learning on AmsterdamUMCdb for ICU Discharge Decision Support: Uniting Intensivists and Data Scientists. Critical care explorations, 3(9), 2021.



Conference publications

el Hassouni, A., Hoogendoorn, M., Eiben, A.E., Muhonen, V., Ciharova, M., Kleiboer, A., Amarti, K., and Riper, H., pH-RL: A personalization architecture to bring reinforcement learning to health practice, Proceedings of the 7th International Conference on Machine Learning, Optimization, and Data Science (LOD), LNCS, 2021.

Grua, E. M., Sanctis, M. D., Malavolta, I., Hoogendoorn, M., and Lago, P.. Social sustainability in the e-health domain via personalized and self-adaptive mobile apps. In Software Sustainability (pp. 301-328). Springer, Cham, 2021.

Klein, J., Bhulai, S., Hoogendoorn, M., and Mei, R.v.d., Plusmine: Dynamic Active Learning with Future-proof Semi-Supervised Learning for Automatic Classification, Proceedings of the 18th IEEE/WIC/ACM International Conference on Web Intelligence, IEEE Computer Society Press, 2021.

Lutscher, D. , el Hassouni, A., Stol, M., and Hoogendoorn, M., Mixing Consistent Deep Clustering, Proceedings of the 7th International Conference on Machine Learning, Optimization, and Data Science (LOD), LNCS, 2021.

Silvestrin, L.P., Pantiskas, L., and Hoogendoorn, M., A Framework for Imbalanced Time-series Forecasting, Proceedings of the 7th International Conference on Machine Learning, Optimization, and Data Science (LOD), LNCS, 2021.



2020



Journal publications

Fleuren, L.M., Thoral, P., Shillan, D., Ercole, A., Elbers, P.W.G., Hoogendoorn, M., Gibbison, M., Klausch, T.L.T., Guo, T., Roggeveen, L.F., Swart, E.L., and Girbes, A.R.J., Machine learning in intensive care medicine: ready for take-off? Intensive Care Medicine, 2020, 46(7), pp. 1486-1488.

Fleuren, L.M., Klausch, T.L.T., Zwager, C.L., Schoonmade, L.J., Guo, T., Roggeveen, L.F., Swart, E.L., Girbes, A.R.J., Thoral, P., Ercole, A., Hoogendoorn, M., and Elbers, P.W.G., Machine Learning for the Prediction of Sepsis, a Systematic Review and Meta-Analysis of Diagnostic Test Accuracy, Intensive Care Medicine, 2020, 46(3), pp.383-400.

den Hengst, F. Grua, E.M., el Hassouni, A., and Hoogendoorn, M., Reinforcement learning for personalization: A systematic literature review, Data Science, IOS Press, 2020.

Friedl, N., Krieger, T., Chevreul, K., Hazo, J.B., Holtzmann, J., Hoogendoorn, M., Kleiboer, A., Mathiasen, K., Urech, A., Riper, H., and Berger, T., Using the Personalized Advantage Index for Individual Treatment Allocation to Blended Treatment or Treatment as Usual for Depression in Secondary Care. Journal of Clinical Medicine, 2020, 9(2), 490.

Funk, B., Shiri Sadeh-Sharvit, S., Fitzsimmons-Craft, E.E., Trockel, M., Monterubio, G.E., Goel, N.J., Balantekin, K.N., Eichen D.M., Flatt, R.E., Firebaugh, M.L., Corinna Jacobi, C., Graham, A.K., Hoogendoorn, M., Wilfley, D.E., and Taylor, C.B., A Framework for Applying Natural Language Processing in Digital Health Interventions, Journal of Medical Internet Research, 2020, to appear.

Polchlopek, O., Koning, N. R., Buechner, F. L., Crone, M. R., Numans, M. E., & Hoogendoorn, M. (2020). Quantitative and temporal approach to utilising electronic medical records from general practices in mental health prediction. Computers in Biology and Medicine, 2021, vol. 125.



Conference publications

el Hassouni, A., Hoogendoorn, M., Eiben, A.E., and Muhonen, V., Structural and Functional Representativity of GANs for Data Generation in Sequential Decision Making (nominated for best paper award), Proceedings of The 6th International Conference on Machine Learning, Optimization, and Data Science (LOD), 2020, pp. 458-471. Springer.

Romero, D. W., Bekkers, E., Tomczak, J.M., and Hoogendoorn, M., Attentive Group Equivariant Convolutional Networks. In: Proceedings of the International Conference on Machine Learning (ICML), 2020.

Romero, D. W. and Hoogendoorn, M., Co-Attentive Equivariant Neural Networks: Focusing Equivariance On Transformations Co-Occurring in Data. In: Proceedings of the International Conference on Learning Representations (ICLR), 2020.

Tabatabaei, S.A., Klein, J., and Hoogendoorn, M., Estimating the F1 score for Learning from Positive and Unlabeled Examples (nominated for best paper award), Proceedings of The 6th International Conference on Machine Learning, Optimization, and Data Science (LOD), , 2020, pp. 150-161, Springer.



2019



Conference publications

Grua, E.M., Hoogendoorn, M., Malavolta, I., Lago, P., and Eiben, A.E., CluStream-GT: Online Clustering for Personalization in the Health Domain, In: Proceedings of the 18th IEEE/WIC/ACM International Conference on Web Intelligence, IEEE Computer Society Press, 2019, pp. 270-275.

el Hassouni, A., Hoogendoorn, M., Eiben, A.E., van Otterlo, M., and Muhonen, V., End-to-end Personalization of Digital Health Interventions using Raw Sensor Data with Deep Reinforcement Learning, In: Proceedings of the 18th IEEE/WIC/ACM International Conference on Web Intelligence, IEEE Computer Society Press, 2019, pp. 258-264.

den Hengst, F., Hoogendoorn, M., van Harmelen, F., and Bosman, J., Reinforcement Learning for Personalized Dialogue Management, In: Proceedings of the 18th IEEE/WIC/ACM International Conference on Web Intelligence, IEEE Computer Society Press, 2019, pp. 59-67.

Hoogendoorn, M., van Breda, W., and Ruwaard, J., GP-HD: Using Genetic Programming to Generate Dynamical Systems Models for Health Care, In: Proceedings of the 18th IEEE/WIC/ACM International Conference on Web Intelligence, IEEE Computer Society Press, 2019, pp. 1-8. [pdf].

Lu, X., Tabatabaei, S.A., Hoogendoorn, M., and Reijers, H.A., Trace Clustering on Very Large Event Data in Healthcare using Frequent Sequence Patterns, In: International Conference on Business Process Management 2019, Lecture Notes in Computer Science, vol 11675, Springer, pp. 198-215.

Mensah, C., Klein, J., Bhulai, S., Hoogendoorn, M., and Van Der Mei, R., Detecting Fraudulent Bookings of Online Travel Agencies with Unsupervised Machine Learning, In: Proceedings of the 32nd International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems, IEA-AIE 2019, Lecture Notes in Computer Science, vol. 11606, Springer, pp. 334-346.

Silvestrin, L., Hoogendoorn, M., and Koole, G., A Comparative Study of State-of-the-Art Machine Learning Algorithms for Predictive Maintenance, In: 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019, IEEE Computer Society Press, to appear, 2019.

Tabatabaei, S.A., Lu, X., Hoogendoorn, M., and Reijers, H.A., Identifying Patient Groups based on Frequent Patterns of Patient Samples, In: 2019 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom), IEEE Computer Society Press, to appear, 2019.

Zonta, A., Smit, S.K., Hoogendoorn., M, and Eiben, A.E., Generation of Human-Like Movements Based on Environmental Features, In: 2019 IEEE Symposium Series on Computational Intelligence (SSCI), 2019, IEEE Computer Society Press, to appear, 2019.



2018



Journal publications

Becker, D., van Breda, W., Funk, B., Hoogendoorn, M., Ruwaard, J., and Riper, H., Predictive modeling in e-mental health: A common language framework. Internet interventions, 12, 2018, pp. 57-67.

Breda, W. van, Bremer, V., Becker, D., Hoogendoorn, M., Funk, B., Ruwaard, J., and Riper, H., Predicting Therapy Success For Treatment as Usual and Blended Treatment in the Domain of Depression, Internet Interventions, vol. 12, 2018 (online since 2017), pp. 100-104.

Bremer, V., Becker, D., Kolovos, S., Funk, B., van Breda, W., Hoogendoorn, M., and Riper, H. Predicting therapy success and costs using baseline characteristics - An Approach for personalized treatment recommendations. Journal of Medical Internet Research, vol. 20, 2018. Mikus, A. Hoogendoorn, M., Rocha, A., Gama, J., Ruwaard, J., and Riper, H., Predicting Short Term Mood Developments among Depressed Patients using Adherence and Ecological Momentary Assessment Data, Internet Interventions, vol. 12, 2018 (online since 2017), pp. 105-110.



Conference publications

el Hassouni, A., Hoogendoorn, M., van Otterlo, M., and Barbaro, E., Personalization of health interventions using cluster-based reinforcement learning. In International Conference on Principles and Practice of Multi-Agent Systems, Springer, 2018, pp. 467-475.

el Hassouni, A., Hoogendoorn, M., and Muhonen, V., Using Generative Adversarial Networks to Develop a Realistic Human Behavior Simulator. In International Conference on Principles and Practice of Multi-Agent Systems, Springer, 2018, pp. 476-483.

Grua, E. M., & Hoogendoorn, M., Exploring Clustering Techniques for Effective Reinforcement Learning based Personalization for Health and Wellbeing. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI), 2018, IEEE, pp. 813-820.

Jiang, J., Hoogendoorn, M., Kong, Q., Roijers, D. M., and Gilbert, N. (2018, November). Predicting Appliance Usage Status In Home Like Environments. In 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP), 2018, IEEE, pp. 1-5.

Klein, J., Bhulai, S., Hoogendoorn, M., Van Der Mei, R., and Hinfelaar, R., Detecting Network Intrusion Beyond 1999: Applying Machine Learning Techniques to a Partially Labeled Cybersecurity Dataset, In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), 2018, IEEE, pp. 784-787.

Tabatabaei, S. A., Hoogendoorn, M., & van Halteren, A. (2018, October). Narrowing Reinforcement Learning: Overcoming the Cold Start Problem for Personalized Health Interventions. In International Conference on Principles and Practice of Multi-Agent Systems, 2018, Springer, pp. 312-327.



2017



Books

Hoogendoorn, M. and Funk, B., Machine Learning for the Quantified Self - On the Art of Learning from Sensory Data, Springer, 2017.



Journal publications

Breda, W. van, Hoogendoorn, M., Eiben, A.E., and Berking, M., Assessment of Temporal Prediction Models for Health-Care using a Formal Method, Computers in Biology and Medicine, vol. 87, 2017, pp. 347-357. Hoogendoorn, M., Berger, T., Schulz, A., Stolz, T., and Szolovits, P., Predicting Social Anxiety Treatment Outcome based on Therapeutic Email Conversations. IEEE Journal of Biomedical and Health Informatics, vol. 21, 2017, pp. 1449-1459



Conference publications

Amirkhan, R., Hoogendoorn, M., Numans, M.E., and Moons, L.M.G., Using Recurrent Neural Networks to Predict Colorectal Cancer among Patients, 2017 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, 2017, pp. 1-8.



2016



Journal publications

Hoogendoorn, M., Szolovits, P., Moons, L.M.G., Numans, M.E., Utilizing uncoded consultation notes from electronic medical records for predictive modeling of colorectal cancer. Artificial Intelligence in Medicine, vol. 69, pp. 53-61, 2016.

Kop, R., Hoogendoorn, M., ten Teije, A, Buechner, F.L., Slottje, P., Moons, L.M., and Numans, M.E., Predictive Modeling of Colorectal Cancer using a Dedicated Pre-processing Pipeline on Routine Electronic Medical Records, Computers in Biology and Medicine, vol. 76, pp.30-38, 2016.



Conference publications

Breda, W. van, Hoogendoorn, M., Eiben, A.E., Andersson, G., Riper, H., Ruwaard, J. and Vernmark, K., A feature representation learning method for temporal datasets, IEEE SSCI 2016, IEEE, pp. 1-8, 2016.

Breda, W. van, Pastor, J., Hoogendoorn, M., Ruwaard, J., Asselbergs, J., and Riper, H., Exploring and Comparing Machine Learning Approaches for Predicting Mood over Time. In: In Innovation in Medicine and Healthcare, Springer, pp. 37-47, 2016.

Hoogendoorn, M., Hassouni, A., Mok, K., Ghassemi, M., and Szolovits, P., Prediction using Patient Comparison vs. Modeling: A Case Study for Mortality Prediction, 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, pp. 2464-2467, 2016.



2015



Journal publications

Bosse, T., and Hoogendoorn, M. (eds.) Special Issue on Advances in Applied Artificial Intelligence, Applied Intelligence, vol. 42, 2015.

Both, F., Hoogendoorn, M., Klein, M.C.A., and Treur, J. A generic computational model of mood regulation and its use to model therapeutical interventions. Biologically Inspired Cognitive Architectures, vol. 13, 2015, pp. 17-34.

Karafotias, G., Hoogendoorn, M., and Eiben, A.E., Trends and Challenges in Evolutionary Algorithms Parameter Control, IEEE Transactions on Evolutionary Computing, 2015, vol. 19, 2015, pp. 167-187.

Koopmanschap, R., Hoogendoorn, M., Roessingh, J.J., Tailoring a Cognitive Model for Situation Awareness using Machine Learning. Applied Intelligence, vol. 42, 2015, pp. 36-48.



Conference publications

Breda, W. van, Hoogendoorn, M., Eiben, A.E., and Berking, M., An Evaluation Framework for the Comparison of Fine-Grained Predictive Models in Health Care. In: Holms, H.J. et al. (eds), Proceedings of the 15th Conference on Artificial Intelligence in Medicine (AIME 2015), Lecture Notes in Computer Science vol. 9105, Springer Verlag, 2015, pp. 148-152.

Gao, S., and Hoogendoorn, M., Using Evolutionary Algorithms to Personalize Controllers in Ambient Intelligence, In: Proceedings of the 6th International Symposium on Ambient Intelligence (ISAMI 2015), Advances in Intelligent Systems and Computing, Springer Verlag, 2015, pp. 1-11.

Karafotias, G., Hoogendoorn, M., and Eiben, A.E., Evaluating Reward Definitions for Parameter Control. In: Mora, A.M. and Squillero, G. (eds.), Applications of Evolutionary Computation - 18th European Conference, EvoApplications 2015, Lecture Notes in Computer Science vol. 9028, 2015, pp. 667-680.

Kop, R., Hoogendoorn, M., Moons, L., Numans, M.E. and Teije, A. ten, On the Advantage of Using Dedicated Data Mining Techniques to Predict Colorectal Cancer. In: Holms, H.J. et al. (eds), Proceedings of the 15th Conference on Artificial Intelligence in Medicine (AIME 2015), Lecture Notes in Computer Science vol. 9105, Springer Verlag, 2015, pp. 133-142.

Kop, R., Toubman, A., Hoogendoorn, M., and Roessingh, J.J., Evolutionary Dynamic Scripting: Adaptation of Expert Rule Bases for Serious Games. In: Proceedings of the 28th International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems, IEA-AIE 2015, Lecture Notes in Computer Science vol. 9101, Springer Verlag, 2015, pp. 53-62.



2014



Journal publications

Hoogendoorn, M., Jaffry, S.W., Maanen, P.P. van, and Treur, J., Design and Validation of a Relative Trust Model. Knowledge-Based Systems Journal, vol. 57, 2014, pp. 81-94.



Conference publications

Hoogendoorn, M., Moons, L.G., Numans, M., and Sips, R.J., Utilizing Data Mining for Predictive Modeling of Colorectal Cancer using Electronic Medical Records. In: Slezak, D., et al. (eds.) Brain Informatics and Health 2014, Springer LNCS, vol. 8609, 2014, pp. 132-141.

Hoogendoorn, M., Schut, M.C., and Treur, J., Computational Modeling of Organization in Honeybee Societies Based on Adaptive Role Allocation. In: Devillers, J. (ed.), In Silico Bees. Taylor & Francis, 2014, pp. 27-44.

Karafotias, G., Eiben, A.E., and Hoogendoorn, M., Generic Parameter Control with Reinforcement Learning. In: GECCO '14: Proceedings of the 2014 Genetic and Evolutionary Computation Conference, ACM Press, 2014, pp. 1319-1326.

Karafotias, G., Hoogendoorn, M., and Weel, B., Comparing Generic Parameter Controllers for EAs. In: IEEE Symposium on Foundations of Computational Intelligence (FOCI), IEEE Computer Society Press, 2014, pp. 46-53.

Kop, R., Hoogendoorn, M., and Klein, M.C.A., A Personalized Support Agent for Depressed Patients: Forecasting Patient Behavior Using a Mood and Coping Model. In: Slezak, D., Dunin-Keplicz, B., Lewis, M., and Terano, T. (eds.), Proceedings of the 2014 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2014), IEEE Computer Society Press, 2014, pp. 302-309.

Oudendag, D., Jongeneel, R., and Hoogendoorn, M., Agent-based modeling of farming behavior, In: Proceedings of the 14th EAAE Conference, 2014.

Oudendag, D., Hoogendoorn, M., and Jongeneel, R., Agent-Based Modeling of Farming Behavior: A Case Study for Milk Quota Abolishment. In: Ali, M. et al (eds.) Modern Advances in Applied Intelligence, Proceedings of the 27th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA-AIE 2014, Springer Verlag LNCS vol. 8481, 2014, pp. 11-20.

Wilcke, X., Hoogendoorn, M., and Roessingh, J.J., Co-Evolutionary Learning for Cognitive Computer Generated Entities. In: Ali, M. et al (eds.) Modern Advances in Applied Intelligence, Proceedings of the 27th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA-AIE 2014, Springer Verlag LNCS vol. 8481, 2014, pp. 120-129.



Publications before 2014 can be found here