Mark Hoogendoorn

dr.

19942019
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Research Output 1994 2019

2019

Detecting fraudulent bookings of online travel agencies with unsupervised machine learning

Mensah, C., Klein, J., Bhulai, S., Hoogendoorn, M. & van der Mei, R., 1 Jan 2019, Advances and Trends in Artificial Intelligence. From Theory to Practice - 32nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019, Proceedings. Friedrich, G., Ali, M., Wotawa, F., Pill, I. & Koitz-Hristov, R. (eds.). Springer Verlag, p. 334-346 13 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11606 LNAI).

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Unsupervised Learning
Learning systems
Anomaly Detection
Machine Learning
Transportation charges

Detecting Network Intrusion beyond 1999: Applying Machine Learning Techniques to a Partially Labeled Cybersecurity Dataset

Klein, J., Bhulai, S., Hoogendoorn, M., Van Der Mei, R. & Hinfelaar, R., 10 Jan 2019, Proceedings - 2018 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2018. IEEE, p. 784-787 4 p. 8609692

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Open Access
File
Intrusion detection
Learning systems

Exploring Clustering Techniques for Effective Reinforcement Learning based Personalization for Health and Wellbeing

Grua, E. M. & Hoogendoorn, M., 28 Jan 2019, Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018. Sundaram, S. (ed.). Institute of Electrical and Electronics Engineers Inc., p. 813-820 8 p. 8628621

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Personalization
Reinforcement learning
Reinforcement Learning
Health
Clustering
2018

Basics of sensory data

Hoogendoorn, M. & Funk, B., 1 Jan 2018, Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data. Springer/Verlag, p. 15-24 10 p. (Cognitive Systems Monographs; vol. 35).

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review

Learning systems
Sensors

Clustering

Hoogendoorn, M. & Funk, B., 1 Jan 2018, Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data. Springer/Verlag, p. 73-100 28 p. (Cognitive Systems Monographs; vol. 35).

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review

Discussion

Hoogendoorn, M. & Funk, B., 1 Jan 2018, Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data. Springer/Verlag, p. 217-221 5 p. (Cognitive Systems Monographs; vol. 35).

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review

Learning systems

Handling noise and missing values in sensory data

Hoogendoorn, M. & Funk, B., 1 Jan 2018, Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data. Springer/Verlag, p. 25-50 26 p. (Cognitive Systems Monographs; vol. 35).

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review

Low pass filters
Kalman filters
Principal component analysis

Narrowing reinforcement learning: Overcoming the cold start problem for personalized health interventions

Tabatabaei, S. A., Hoogendoorn, M. & van Halteren, A., 2018, PRIMA 2018: Principles and Practice of Multi-Agent Systems: 21st International Conference, Tokyo, Japan, October 29-November 2, 2018, Proceedings. Oren, N., Sakurai, Y., Noda, I., Cao Son, T., Miller, T. & Savarimuthu, B. T. (eds.). Springer/Verlag, p. 312-327 16 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11224 LNAI).

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Reinforcement learning
Reinforcement Learning
Personalization
Health
Simulators

Personalization of health interventions using cluster-based reinforcement learning

el Hassouni, A., Hoogendoorn, M., van Otterlo, M. & Barbaro, E., 2018, PRIMA 2018 Principles and Practice of Multi-Agent Systems: 21st International Conference, Tokyo, Japan, October 29-November 2, 2018, Proceedings. Oren, N., Sakurai, Y., Noda, I., Cao Son, T., Miller, T. & Savarimuthu, B. T. (eds.). Springer/Verlag, p. 467-475 9 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11224 LNAI).

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Personalization
Reinforcement learning
Reinforcement Learning
Health
Learning algorithms

Predicting short term mood developments among depressed patients using adherence and ecological momentary assessment data

Mikus, A., Hoogendoorn, M., Rocha, A., Gama, J., Ruwaard, J. & Riper, H., Jun 2018, In : Internet Interventions. 12, p. 105-110 6 p.

Research output: Contribution to JournalArticleAcademicpeer-review

Open Access
Patient Compliance
Cell Phones
Artificial Intelligence
Major Depressive Disorder
Diagnostic and Statistical Manual of Mental Disorders

Predicting therapy success and costs for personalized treatment recommendations using baseline characteristics: Data-driven analysis

Bremer, V., Becker, D., Kolovos, S., Funk, B., Van Breda, W., Hoogendoorn, M. & Riper, H., 21 Aug 2018, In : Journal of Medical Internet Research. 20, 8, p. 1-11 11 p., e10275.

Research output: Contribution to JournalArticleAcademicpeer-review

Open Access
Health Care Costs
Costs and Cost Analysis
Therapeutics
Cost-Benefit Analysis
Decision Making

Predictive modeling in e-mental health: A common language framework

Becker, D., van Breda, W., Funk, B., Hoogendoorn, M., Ruwaard, J. & Riper, H., 1 Jun 2018, In : Internet Interventions. 12, p. 57-67 11 p.

Research output: Contribution to JournalReview articleAcademicpeer-review

Open Access
Mental Health
Language
Research
Data Mining
Health Services Research

Predictive modeling with notion of time

Hoogendoorn, M. & Funk, B., 1 Jan 2018, Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data. Springer/Verlag, p. 167-202 36 p. (Cognitive Systems Monographs; vol. 35).

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review

Recurrent neural networks
Supervised learning
Time series
Dynamical systems

Predictive modeling without notion of time

Hoogendoorn, M. & Funk, B., 1 Jan 2018, Cognitive Systems Monographs. Springer/Verlag, Vol. 35. p. 123-165 43 p. (Cognitive Systems Monographs; vol. 35).

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review

Feedforward neural networks
Supervised learning
Decision trees
Support vector machines
Feature extraction

Reinforcement learning to provide feedback and support

Hoogendoorn, M. & Funk, B., 1 Jan 2018, Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data. Springer/Verlag, p. 203-214 12 p. (Cognitive Systems Monographs; vol. 35).

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review

Reinforcement learning
Feedback
Sensors

Using generative adversarial networks to develop a realistic human behavior simulator

el Hassouni, A., Hoogendoorn, M. & Muhonen, V., 2018, PRIMA 2018 Principles and Practice of Multi-Agent Systems: 21st International Conference, Tokyo, Japan, October 29-November 2, 2018, Proceedings. Oren, N., Sakurai, Y., Noda, I., Cao Son, T., Miller, T. & Savarimuthu, B. T. (eds.). Springer/Verlag, p. 476-483 8 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11224 LNAI).

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Human Behavior
Simulator
Simulators
Simulation Environment
Reinforcement learning

Using recurrent neural networks to predict colorectal cancer among patients

Amirkhan, R., Hoogendoorn, M., Numans, M. E. & Moons, L., 5 Feb 2018, 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., Vol. 2018-January. p. 1-8 8 p.

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Electronic medical equipment
Colorectal Cancer
Recurrent neural networks
Predictive Model
Recurrent Neural Networks
2017

A feature representation learning method for temporal datasets

van Breda, W., Hoogendoorn, M., Eiben, G., Andersson, G., Riper, H., Ruwaard, J. & Vernmark, K., 9 Feb 2017, 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016. Institute of Electrical and Electronics Engineers, Inc., p. 1-8 8 p. 7849890

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Health care
Health
Healthcare
Predictive Modeling
Predictive Model

Assessment of temporal predictive models for health care using a formal method

van Breda, W., Hoogendoorn, M., Eiben, A. E. & Berking, M., 1 Aug 2017, In : Computers in Biology and Medicine. 87, p. 347-357 11 p.

Research output: Contribution to JournalArticleAcademicpeer-review

Formal methods
Health care
Delivery of Health Care
Health
Mental Health

Feature engineering based on sensory data

Hoogendoorn, M. & Funk, B., 27 Sep 2017, Machine Learning for the Quantified Self : On the Art of Learning from Sensory Data. Springer/Verlag, p. 51-70 20 p. (Cognitive Systems Monographs; vol. 35).

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review

Introduction

Hoogendoorn, M. & Funk, B., 27 Sep 2017, Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data. Springer/Verlag, Vol. 35. p. 1-12 12 p. (Cognitive Systems Monographs; vol. 35).

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review

Learning systems

Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data

Hoogendoorn, M. & Funk, B., 2017, Springer. 231 p.

Research output: Book / ReportBookAcademicpeer-review

Mathematical foundations for supervised learning

Hoogendoorn, M. & Funk, B., 27 Sep 2017, Machine Learning or the Quantified Self: On the Art of Learning from Sensory Data. Springer/Verlag, Vol. 35. p. 101-121 21 p. (Cognitive Systems Monographs; vol. 35).

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review

Supervised learning
Learning systems

Predicting therapy success for treatment as usual and blended treatment in the domain of depression

van Breda, W., Bremer, V., Becker, D., Hoogendoorn, M., Funk, B., Ruwaard, J. & Riper, H., 2017, In : Internet Interventions.

Research output: Contribution to JournalArticleAcademicpeer-review

Open Access
Depression
Therapeutics
Area Under Curve
Cost-Benefit Analysis
Mental Health
2016

Exploring and comparing machine learning approaches for predicting mood over time

van Breda, W., Pastor, J., Hoogendoorn, M., Ruwaard, J., Asselbergs, J. & Riper, H., 2016, Innovation in Medicine and Healthcare 2016. Springer Science and Business Media Deutschland GmbH, Vol. 60. p. 37-47 11 p. (Smart Innovation, Systems and Technologies; vol. 60).

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Learning systems
Health
Mobile phones
Sensors
Mood

Predicting Social Anxiety Treatment Outcome based on Therapeutic Email Conversations

Hoogendoorn, M., Berger, T., Schulz, A., Stolz, T. & Szolovits, P., 2016, In : Journal of Biomedical and Health Informatics. p. 1449-1459 11 p.

Research output: Contribution to JournalArticleAcademicpeer-review

Electronic mail
Learning algorithms
Learning systems
Anxiety
Health

Prediction using patient comparison vs. modeling: A case study for mortality prediction

Hoogendoorn, M., El Hassouni, A., Mok, K., Ghassemi, M. & Szolovits, P., 13 Oct 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016. Institute of Electrical and Electronics Engineers, Inc., Vol. 2016-October. p. 2464-2467 4 p. 7591229

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Electronic medical equipment
Electronic Health Records
Mortality
Intensive care units
Learning systems

Predictive modeling of colorectal cancer using a dedicated pre-processing pipeline on routine electronic medical records

Kop, R., Hoogendoorn, M., ten Teije, A., Büchner, F. L., Slottje, P., Moons, L. M. G. & Numans, M. E., 1 Sep 2016, In : Computers in Biology and Medicine. 76, p. 30-38 9 p.

Research output: Contribution to JournalArticleAcademicpeer-review

Electronic medical equipment
Electronic Health Records
Colorectal Neoplasms
Pipelines
Processing
2015

A generic computational model of mood regulation and its use to model therapeutical interventions

Both, F., Hoogendoorn, M., Klein, M. C. A. & Treur, J., 1 Jul 2015, In : Biologically Inspired Cognitive Architectures. 13, p. 17-34 18 p.

Research output: Contribution to JournalArticleAcademicpeer-review

Patient Simulation
Therapeutics
Homeostasis
Depression
Experiments

An Evaluation Framework for the Comparison of Fine-Grained Predictive Models in Health Care.

van Breda, W. R. J., Hoogendoorn, M., Eiben, A. E. & Berking, M., 2015, In : Lecture Notes in Computer Science. 9105, p. 148-152

Research output: Contribution to JournalArticleAcademicpeer-review

Evaluating Reward Definitions for Parameter Control

Karafotias, G., Hoogendoorn, M. & Eiben, A. E., 2015, In : Lecture Notes in Computer Science. 9028, p. 667-680

Research output: Contribution to JournalArticleAcademicpeer-review

Evolutionary Dynamic Scripting: Adaptation of Expert Rule Bases for Serious Games

Kop, R., Toubman, A., Hoogendoorn, M. & Roessingh, J. J., 2015, In : Lecture Notes in Computer Science. 9101, p. 53-62

Research output: Contribution to JournalArticleAcademicpeer-review

On the Advantage of Using Dedicated Data Mining Techniques to Predict Colorectal Cancer

Kop, R., Hoogendoorn, M., Moons, L. N. G., Numans, M. E. & ten Teije, A. C. M., 2015, In : Lecture Notes in Computer Science. 9105, p. 133-142

Research output: Contribution to JournalArticleAcademicpeer-review

Parameter Control in Evolutionary Algorithms: Trends and Challenges

Karafotias, G., Hoogendoorn, M. & Eiben, A. E., 1 Apr 2015, In : IEEE Transactions on Evolutionary Computation. 19, 2, p. 167-187 21 p., 6747993.

Research output: Contribution to JournalArticleAcademicpeer-review

Evolutionary algorithms
Control Parameter
Evolutionary Algorithms
Recommendations
Trends

Special issue on advances in applied artificial intelligence

Bosse, T. & Hoogendoorn, M., 2015, In : Applied Intelligence. 42, 1, p. 1-2

Research output: Contribution to JournalComment / Letter to the editorAcademic

Tailoring a Cognitive Model for Situation Awareness using Machine Learning

Koopmanschap, R., Hoogendoorn, M. & Roessingh, J. J., 2015, In : Applied Intelligence. 42, 1, p. 36-48 13 p.

Research output: Contribution to JournalArticleAcademicpeer-review

Learning systems
Intelligent agents
Reinforcement learning
Bayesian networks
Air

Using Evolutionary Algorithms to Personalize Controllers in Ambient Intelligence

Gao, S. & Hoogendoorn, M., 2015, Ambient Intelligence - Software and Applications - 6th International Symposium on Ambient Intelligence, ISAmI 2015. Mohamed, A., Novais, P., Pereira, A., Villarrubia-Gonzalez, G. & Fernandez-Caballero, A. (eds.). Springer/Verlag, Vol. 376. p. 1-11 11 p. (Advances in Intelligent Systems and Computing; vol. 376).

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Evolutionary algorithms
Controllers
Learning systems
Ambient intelligence

Utilizing uncoded consultation notes from electronic medical records for predictive modeling of colorectal cancer

Hoogendoorn, M., Szolovits, P., Moons, L. M. G. & Numans, M. E., 6 Nov 2015, In : Artificial Intelligence in Medicine. 69, p. 53-61 9 p.

Research output: Contribution to JournalArticleAcademicpeer-review

Open Access
Electronic medical equipment
Electronic Health Records
Colorectal Neoplasms
Referral and Consultation
Ontology
2014

Agent-based modeling of farming behavior

Oudendag, D., Hoogendoorn, M. & Jongeneel, R., 2014, Proceedings of the 14th EAAE Conference.

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Agent-Based Modeling of Farming Behavior: A Case Study for Milk Quota Abolishment

Hoogendoorn, M. & Oudendag, D., 2014, In : Lecture Notes in Computer Science. 8481, p. 11-20

Research output: Contribution to JournalArticleAcademicpeer-review

A Personalized Support Agent for Depressed Patients

Kop, R., Hoogendoorn, M. & Klein, M. C. A., 2014, Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 03. IEEE Computer Society, p. 302-309

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Open Access
File

Co-evolutionary Learning for Cognitive Computer Generated Entities

Wilcke, W. X., Hoogendoorn, M. & Roessingh, J. J. M., 2014, In : Lecture Notes in Computer Science. 8482, 2, p. 120-129

Research output: Contribution to JournalArticleAcademicpeer-review

Evolutionary Learning
Learning systems
Machine Learning
Cognitive Models
Coevolution

Comparing Generic Parameter Controllers for EAs

Karafotias, G. & Hoogendoorn, M., 2014, IEEE Symposium Series on Computational Intelligence (SSCI '14). IEEE, p. 46-53

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Evolutionary algorithms
Controllers
Reinforcement learning
Experiments

Computational Modeling of Organization in Honeybee Societies Based on Adaptive Role Allocation

Hoogendoorn, M., Schut, M. C. & Treur, J., 2014, In Silico Bees. Devillers, J. (ed.). Taylor and Fancis, p. 27-44

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review

Design and Validation of a Relative Trust Model

Hoogendoorn, M., Jaffry, S. W., van Maanen, P. P. & Treur, J., 2014, In : Knowledge-Based Systems. 57, p. 81-95

Research output: Contribution to JournalArticleAcademicpeer-review

Intelligent agents
Parameter estimation
Trust model
Competitors
Benchmark

Generic parameter control with reinforcement learning

Karafotias, G., Eiben, A. E. & Hoogendoorn, M., 2014, 2014 conference on Genetic and evolutionary computation (GECCO '14).. ACM, p. 1319-1326

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Utilizing Data Mining for Predictive Modeling of Colorectal Cancer using Electronic Medical Records

Hoogendoorn, M., Moons, L. G., Numans, M. E. & Sips, R. J., 2014, In : Lecture Notes in Computer Science. 8609, p. 132-141

Research output: Contribution to JournalArticleAcademicpeer-review

Predictive Modeling
Electronic medical equipment
Colorectal Cancer
Predictive Model
Data mining
2013

Adaptive autonomy in unmanned ground vehicles using trust models

Toubman, A., Hoogendoorn, M. & van Maanen, P. P., 2013, Human Aspects in Ambient Intelligence. Bosse, T., Cook, D. J., Neerincx, M. & Sadri, F. (eds.). p. 21-37 (Atlantis Ambient and Pervasive Intelligence; no. 8).

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review

Agent-based Modelling of Social Emotional Decision Making in Emergency Situations

Bosse, T., Hoogendoorn, M., Klein, M. C. A., Sharpanskykh, A., Treur, J., van der Wal, C. N. & van Wissen, A., 2013, Co-evolution of Intelligent Socio-technical Systems: Modelling and Applications in Large Scale Emergency and Transport Domains. Springer/Verlag, p. 79-117 39 p. (Understanding Complex Systems).

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review

Decision making

Ambient Support by a Personal Coach for Exercising and Rehabilitation

Bobbert, M. F., Hoogendoorn, M., van Soest, A. J., Stebletsova, V. & Treur, J., 2013, Human Aspects in Ambient Intelligence. Bosse, T., Cook, D. J., Neerincx, M. & Sadri, F. (eds.). Atlantis Press, p. 89-106 (Atlantis Ambient and Pervasive Intelligence; no. 8).

Research output: Chapter in Book / Report / Conference proceedingChapterAcademic