  | Kumar, N., Marée, R., Geurts, P., & Muller, M. (2023). Recent Advances in Bioimage Analysis Methods for Detecting Skeletal Deformities in Biomedical and Aquaculture Fish Species. Biomolecules. doi:10.3390/biom13121797  Peer Reviewed verified by ORBi |
 | Claes, Y., Huynh-Thu, V. A., & Geurts, P. (2023). Knowledge-Guided Additive Modeling for Supervised Regression. Lecture Notes in Computer Science. doi:10.1007/978-3-031-45275-8_5 Peer reviewed |
 | Kumar, N., Claudia Di Biagio, Zachary Dellacqua, Raman, R., Arianna Martini, Clara Boglione, Muller, M., Geurts, P., & Marée, R. (2023). Empirical Evaluation of Deep Learning Approaches for Landmark Detection in Fish Bioimages. In Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science. Cham, Unknown/unspecified: Springer. doi:10.1007/978-3-031-25069-9_31 Peer reviewed |
 | Huynh-Thu, V. A., & Geurts, P. (2023). Optimizing model-agnostic random subspace ensembles. Machine Learning. doi:10.1007/s10994-023-06427-5 Peer Reviewed verified by ORBi |
 | Mormont, R., Testouri, M., Marée, R., & Geurts, P. (2022). Relieving pixel-wise labeling effort for pathology image segmentation with self-training. In Lecture Notes in Computer Science. Genève, Switzerland: Springer Cham. doi:10.1007/978-3-031-25082-8_39 Peer reviewed |
 | Sutera, A., Louppe, G., Huynh-Thu, V. A., Wehenkel, L., & Geurts, P. (2021). From global to local MDI variable importances for random forests and when they are Shapley values. Advances in Neural Information Processing Systems. Peer Reviewed verified by ORBi |
 | Sabatelli, M., & Geurts, P. (2021). On The Transferability of Deep-Q Networks. Deep Reinforcement Learning Workshop of the 35th Conference on Neural Information Processing Systems. Peer reviewed |
 | Navdeep Kumar, Zachary Dellacqua, Clara Boglione, Arianna Martini, Muller, M., Geurts, P., & Marée, R. (04 October 2021). Towards Setting up of an Automatic Recognition System for Vertebrae and Opercular Anomalies in Reared Gilthead Seabream(Sparus aurata) [Poster presentation]. Aquaculture Europe 2021, Funchal, Madeira, Portugal. Editorial reviewed |
 | Kumar, N., Carletti, A., Gavaia, P. J., Muller, M., Cancela, L., Geurts, P., & Marée, R. (2021). Deep Learning approaches for Head and Operculum Segmentation in Zebrafish Microscopy Images. In Part of the Lecture Notes in Computer Science book series (LNIP, volume 13052). Switzerland: Springer. doi:10.1007/978-3-030-89128-2_15 Peer reviewed |
 | Sabatelli, M., Kestemont, M., & Geurts, P. (2021). On the Transferability of Winning Tickets in Non-Natural Image Datasets. 16th International Conference on Computer Vision Theory and Applications - VISAPP 2021. Peer reviewed |
 | Leroy, P., Ernst, D., Geurts, P., Louppe, G., Pisane, J., & Sabatelli, M. (2021). QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning. In Proceedings of the AAAI-21 Workshop on Reinforcement Learning in Games (pp. 8). Peer reviewed |
 | Sabatelli, M., Banar, N., Cocriamont, M., Coudyzer, E., Lasaracina, K., Daelemans, W., Geurts, P., & Kestemont, M. (February 2021). Advances in Digital Music Iconography: Benchmarking the detection of musical instruments in unrestricted, non-photorealistic images from the artistic domain. Digital Humanities Quarterly, 15 (1). Peer Reviewed verified by ORBi |
 | Begon, J.-M., & Geurts, P. (2021). Sample-Free White-Box Out-of-Distribution Detection for Deep Learning. IEEE Conference on Computer Vision and Pattern Recognition. Proceedings. doi:10.1109/CVPRW53098.2021.00367 Peer reviewed |
 | Slavkov, I., Petkovic, M., Geurts, P., Kocev, D., & Dzeroski, S. (07 December 2020). Error curves for evaluating the quality of feature rankings. PeerJ Computer Science, 6 (e310), 39. doi:10.7717/peerj-cs.310 Peer Reviewed verified by ORBi |
 | Sabatelli, M., Louppe, G., Geurts, P., & Wiering, M. (2020). The Deep Quality-Value Family of Deep Reinforcement Learning Algorithms. International Joint Conference on Neural Networks (IJCNN 2020). Peer reviewed |
 | Mormont, R., Geurts, P., & Marée, R. (2020). Multi-task pre-training of deep neural networks for digital pathology. IEEE Journal of Biomedical and Health Informatics. doi:10.1109/JBHI.2020.2992878 Peer Reviewed verified by ORBi |
  | Vecoven, N., Begon, J.-M., Sutera, A., Geurts, P., & Huynh-Thu, V. A. (2020). Nets versus trees for feature ranking and gene network inference. In Proceeding of the 23rd International Conference on Discovery Science (DS 2020). Springer. doi:10.1007/978-3-030-61527-7_16 Peer reviewed |
 | Sabatelli, M., Louppe, G., Geurts, P., & Wiering, M. (2019). Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms. Advances in Neural Information Processing Systems. Peer Reviewed verified by ORBi |
 | Sabatelli, M., Kestemont, M., & Geurts, P. (October 2019). Improving the Training of Deep Convolutional Neural Networks for Art Classification: from Transfer Learning to Multi-Task Learning [Paper presentation]. The 6th Digital Humanities (DH) Benelux Conference. |
 | Faux, P., Geurts, P., & Druet, T. (27 June 2019). A Random Forests Framework for Modeling Haplotypes as Mosaics of Reference Haplotypes. Frontiers in Genetics, 10, 562. doi:10.3389/fgene.2019.00562 Peer Reviewed verified by ORBi |
 | Huynh-Thu, V. A., & Geurts, P. (2019). Unsupervised gene network inference with decision trees and Random forests. In G. Sanguinetti & V. A. Huynh-Thu (Eds.), Gene Regulatory Networks. New York, United States - New York: Humana Press. doi:10.1007/978-1-4939-8882-2_8 |
 | Meyer, F., Wehenkel, M., Phillips, C., Geurts, P., HUSTINX, R., Bernard, C., Bastin, C., Salmon, E., & Alzheimer's Disease NeuroImaging Initiative. (2019). Characterization of a temporoparietal junction subtype of Alzheimer’s disease. Human Brain Mapping, 40, 4279-4286. doi:10.1002/hbm.24701 Peer Reviewed verified by ORBi |
 | Sabatelli, M., Louppe, G., Geurts, P., & Wiering, M. (2018). Deep Quality Value (DQV) Learning. Advances in Neural Information Processing Systems. Peer Reviewed verified by ORBi |
 | Sabatelli, M., Kestemont, M., Daelemans, W., & Geurts, P. (2018). Deep Transfer Learning for Art Classification Problems. European Conference on Computer Vision (ECCV), 4th Workshop on Computer VISion for ART Analysis (VISART IV). Peer reviewed |
 | Wehenkel, M., Sutera, A., Bastin, C., Geurts, P.* , & Phillips, C.*. (29 June 2018). Random Forests based group importance scores and their statistical interpretation: application for Alzheimer’s disease. Frontiers in Neuroscience, 12, 411. doi:10.3389/fnins.2018.00411 Peer Reviewed verified by ORBi* These authors have contributed equally to this work. |
 | Olivier, F., Sutera, A., Geurts, P., Fonteneau, R., & Ernst, D. (2018). Phase Identification of Smart Meters by Clustering Voltage Measurements. In Proceedings of the XX Power Systems Computation Conference (PSCC 2018). doi:10.23919/PSCC.2018.8442853 Peer reviewed |
 | Wehenkel, M., Bastin, C., Geurts, P., & Phillips, C. (2018). Computer Aided Diagnosis System Based on Random Forests for the Prognosis of Alzheimer’s Disease. In 1st HBP Student Conference - Transdisciplinary Research Linking Neuroscience, Brain Medicine and Computer Science. Frontiers Media S.A. doi:10.3389/978-2-88945-421-1 Peer reviewed |
 | Huynh-Thu, V. A., & Geurts, P. (21 February 2018). dynGENIE3: dynamical GENIE3 for the inference of gene networks from time series expression data. Scientific Reports, 8, 3384. doi:10.1038/s41598-018-21715-0 Peer Reviewed verified by ORBi |
 | Pliakos, K., Geurts, P., & Vens, C. (2018). Global multi-output decision trees for interaction prediction. Machine Learning, 107, 1257-1281. doi:10.1007/s10994-018-5700-x Peer Reviewed verified by ORBi |
 | Mormont, R., Geurts, P., & Marée, R. (2018). Comparison of deep transfer learning strategies for digital pathology. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE. Peer reviewed |
 | Sutera, A., Châtel, C., Louppe, G., Wehenkel, L., & Geurts, P. (2018). Random Subspace with Trees for Feature Selection Under Memory Constraints. In A. Storkey & F. Perez-Cruz (Eds.), Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (pp. 929-937). Playa Blanca, Spain: PMLR. Peer reviewed |
 | Vandaele, R., Aceto, J., Muller, M., Peronnet, F., Debat, V., Wang, C.-W., Huang, C.-T., Jodogne, S., Martinive, P., Geurts, P., & Marée, R. (2018). Landmark detection in 2D bioimages for geometric morphometrics: a multi-resolution tree-based approach. Scientific Reports, 8 (1), 538. doi:10.1038/s41598-017-18993-5 Peer Reviewed verified by ORBi |
 | Vecoven, N., Begon, J.-M., Huynh-Thu, V. A., & Geurts, P. (2017). Nets versus trees for feature ranking and gene network inference. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/231719. |
 | LAYIOS, N., Delierneux, C., Hego, A., HUART, J., GOSSET, C., LECUT, C., MAES, N., Geurts, P., Joly, A., LANCELLOTTI, P., Albert, A., DAMAS, P., GOTHOT, A., & Oury, C. (December 2017). Sepsis prediction in critically ill patients by platelet activation markers on ICU admission: a prospective pilot study. Intensive Care Medicine Experimental, 5 (1), 32. doi:10.1186/s40635-017-0145-2 Peer Reviewed verified by ORBi |
 | Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., van den Oord, J., Atak, Z. K., Wouters, J., & Aerts, S. (09 October 2017). SCENIC: single-cell regulatory network inference and clustering. Nature Methods, 14, 1083–1086. doi:10.1038/nmeth.4463 Peer Reviewed verified by ORBi |
 | Azrour, S., Pierard, S., Geurts, P., & Van Droogenbroeck, M. (2017). A two-step methodology for human pose estimation increasing the accuracy and reducing the amount of learning samples dramatically. In Advanced Concepts for Intelligent Vision Systems (pp. 3-14). Springer. doi:10.1007/978-3-319-70353-4_1 Peer reviewed |
 | Wehenkel, M., Bastin, C., Geurts, P., & Phillips, C. (28 June 2017). Parceling and tree-based ensemble methods for the prognosis of Alzheimer's disease [Poster presentation]. 23rd Annual Meeting of the Organization for Human Brain Mapping, Vancouver, Canada. |
 | Sutera, A., Joly, A., François-Lavet, V., Qiu, Z., Ernst, D., & Geurts, P. (2017). Simple connectome inference from partial correlation statistics in calcium imaging. In J. Soriano, D. Battaglia, I. Guyon, V. Lemaire, J. Orlandi, ... B. Ray (Eds.), Neural Connectomics Challenge (pp. 23-36). Springer. doi:10.1007/978-3-319-53070-3 Peer reviewed |
 | Wehenkel, M., Bastin, C., Phillips, C.* , & Geurts, P.*. (2017). Tree Ensemble Methods and Parcelling to Identify Brain Areas Related to Alzheimer’s Disease. In 2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI), proceedings. IEEE. doi:10.1109/PRNI.2017.7981513 Peer reviewed* These authors have contributed equally to this work. |
Wehenkel, M., Bastin, C., Phillips, C., & Geurts, P. (08 February 2017). Computer aided diagnosis system based on random forests for the prognosis of Alzheimer's disease [Paper presentation]. 1st HBP Student Conference - Transdisciplinary Research Linking Neuroscience, Brain Medicine and Computer Science, Vienne, Austria. |
 | Begon, J.-M., Joly, A., & Geurts, P. (2017). Globally Induced Forest: A Prepruning Compression Scheme. Proceedings of Machine Learning Research, 70, 420-428. Peer Reviewed verified by ORBi |
 | Vandaele, R., LALLEMAND, F., MARTINIVE, P., GULYBAN, A., JODOGNE, S., COUCKE, P., Geurts, P., & Marée, R. (2017). Automated multimodal volume registration based on supervised 3D anatomical landmark detection. In SCITEPRESS Digital Library. Peer reviewed |
Assent, D., Bourgot, I., Hennuy, B., Geurts, P., Foidart, J.-M., Noël, A., & Maquoi, E. (October 2016). A membrane-type- matrix metalloproteinase (MT1-MMP) - discoidin domain receptor 1 axis regulates collagen-induced apoptosis in breast cancer cells [Poster presentation]. EACR Meeting - Goodbye flat biology : models, mechanisms and microenvironment, Berlin, Germany. |
 | Sutera, A., Châtel, C., Louppe, G., Wehenkel, L., & Geurts, P. (12 September 2016). Random subspace with trees for feature selection under memory constraints [Poster presentation]. The 25th Belgian-Dutch Conference on Machine Learning (Benelearn), Kortrijk, Belgium. Peer reviewed |
 | Begon, J.-M., Joly, A., & Geurts, P. (12 September 2016). Joint learning and pruning of decision forests [Paper presentation]. The 25th Belgian-Dutch Conference on Machine Learning (Benelearn), Kortrijk, Belgium. |
Wehenkel, M., Geurts, P., & Phillips, C. (30 June 2016). Accuracy and interpretability, tree-based machine learning approaches [Poster presentation]. 22nd Annual Meeting of the Organization for Human Brain Mapping, Geneva, Switzerland. |
 | Sutera, A., Louppe, G., Huynh-Thu, V. A., Wehenkel, L., & Geurts, P. (2016). Context-dependent feature analysis with random forests. In Uncertainty In Artificial Intelligence: Proceedings of the Thirty-Two Conference (2016). Peer reviewed |
 | Geurts, P., & Wehenkel, L. (19 April 2016). Comments on: A random forest guided tour. TEST, 25 (2), 247-253. doi:10.1007/s11749-016-0487-1 |
Assent, D., Bourgot, I., Hennuy, B., Geurts, P., Noël, A., Foidart, J.-M., & Maquoi, E. (04 March 2016). A Membrane-Type-1 Matrix Metalloproteinase (MT1-MMP)- Discoïdin Domain Receptor 1 axis regulates collagen-induced apoptosis in breast cancer cells [Poster presentation]. International Joint Meeting of the German and French Societies for Matrix Biology, Freiburg, Germany. |
 | Marée, R., Rollus, L., Stévens, B., Hoyoux, R., Louppe, G., Vandaele, R., Begon, J.-M., Kainz, P., Geurts, P., & Wehenkel, L. (2016). Collaborative analysis of multi-gigapixel imaging data using Cytomine. Bioinformatics, 7. doi:10.1093/bioinformatics/btw013 Peer Reviewed verified by ORBi |
 | Marée, R., Geurts, P., & Wehenkel, L. (2016). Towards Generic Image Classification using Tree-based Learning: an Extensive Empirical Study. Pattern Recognition Letters, 74 (15), 17-23. doi:10.1016/j.patrec.2016.01.006 Peer Reviewed verified by ORBi |
 | Freres, P.* , Wenric, S.* , BOUKERROUCHA, M., Fasquelle, C., Thiry, J., Bovy, N., Struman, I., Geurts, P., COLLIGNON, J., SCHROEDER, H., KRIDELKA, F., LIFRANGE, E., Jossa, V., Bours, V.* , Josse, C.* , & JERUSALEM, G.*. (2015). Circulating microRNA-based screening tool for breast cancer. Oncotarget. doi:10.18632/oncotarget.6786 Peer Reviewed verified by ORBi* These authors have contributed equally to this work. |
Delierneux, C., LAYIOS, N., Hego, A., HUART, J., Joly, A., Geurts, P., DAMAS, P., LECUT, C., GOTHOT, A., & Oury, C. (19 October 2015). Elevated basal levels of platelet-bound fibrinogen predict the occurrence of sepsis in ICU: a prospective study [Paper presentation]. Belgian Society on Thrombosis and Haemostasis (BSTH), Lamot, Belgium. |
 | Du, W., Liao, Y., Tao, N., Geurts, P., Fu, X., & Leduc, G. (October 2015). Rating Network Paths for Locality-Aware Overlay Construction and Routing. IEEE/ACM Transactions on Networking, 23 (5), 1661-1673. doi:10.1109/TNET.2014.2337371 Peer Reviewed verified by ORBi |
 | Liegeois, R., Ziegler, E., Bahri, M. A., Phillips, C., Geurts, P., Gomez, F., Yeo, T., VANHAUDENHUYSE, A., Soddu, A., LAUREYS, S., & Sepulchre, R. (2015). Cerebral functional connectivity periodically (de)synchronizes with anatomical constraints. Brain Structure and Function. doi:10.1007/s00429-015-1083-y Peer Reviewed verified by ORBi |
Delierneux, C., LAYIOS, N., Hego, A., HUART, J., Joly, A., Geurts, P., DAMAS, P., LECUT, C., GOTHOT, A., & Oury, C. (23 June 2015). Prospective analysis of platelet activation markers to predict severe infection and mortality in intensive care units [Poster presentation]. XXV Congress of the International Society on Thrombosis and Haemostasis (ISTH), Toronto, Canada. |
 | Schrynemackers, M., Wehenkel, L., Madan Babu, M., & Geurts, P. (11 May 2015). Classifying pairs with trees for supervised biological network inference. Molecular Biosystems, 11 (8), 2116-2125. doi:10.1039/c5mb00174a Peer Reviewed verified by ORBi |
 | Vandaele, R., Marée, R., COUCKE, P., GULYBAN, A., LALLEMAND, F., Geurts, P., JODOGNE, S., & MARTINIVE, P. (April 2015). Automated Landmark Detection For Rigid Registration Between The Simulation-CT and the Treatment CBCT [Poster presentation]. 3rd ESTRO FORUM, Barcelone, Spain. |
 | Wehenkel, M., Geurts, P., & Phillips, C. (15 March 2015). Tree Ensemble Methods for Computer Aided Diagnosis (CAD) Systems [Poster presentation]. 2nd HBP Education Workshop : Future Medicine, Lausanne, Switzerland. |
 | Assent, D., Bourgot, I., Hennuy, B., Geurts, P., Noël, A., Foidart, J.-M., & Maquoi, E. (2015). A Membrane-Type-1 Matrix Metalloproteinase (MT1-MMP) - Discoidin Domain Receptor 1 Axis Regulates Collagen-Induced Apoptosis in Breast Cancer Cells. PLoS ONE, 10 (3), 0116006. doi:10.1371/journal.pone.0116006 Peer Reviewed verified by ORBi |
 | LAYIOS, N., Delierneux, C., Hego, A., HUART, J., Joly, A., Geurts, P., DAMAS, P., LECUT, C., Gothot, A., & Oury, C. (2015). Erratum: Elevated basal levels of circulating activated platelets predict ICU-acquired sepsis and mortality: a prospective study. Critical Care, 19 (1), 301. doi:10.1186/s13054-015-1005-7 Peer Reviewed verified by ORBi |
 | Jeanray, N., Marée, R., Pruvot, B., Stern, O., Geurts, P., Wehenkel, L., & Muller, M. (2015). Phenotype Classification of Zebrafish Embryos by Supervised Learning. PLoS ONE, 10 (1), 0116989, 1-20. doi:10.1371/journal.pone.0116989 Peer Reviewed verified by ORBi |
 | LAYIOS, N., GOSSET, C., Delierneux, C., Hego, A., HUART, J., Joly, A., Geurts, P., DAMAS, P., Oury, C., & Gothot, A. (2015). Erratum: Prospective immune profiling in critically ill adults: before, during and after severe sepsis and septic shock. Critical Care, 19 (1), 300. doi:10.1186/s13054-015-1006-6 Peer Reviewed verified by ORBi |
 | Ching Wei, W., Cheng-Ta, H., Meng-Che, H., Chu-Hsing, L., Vandaele, R., Sheng-Wei, C., Wei-Cheng, L., Marée, R., Guoyan, Z., Ghassan, H., Vrtovec, T., JODOGNE, S., Geurts, P., Chengwen, C., Hengameh, M., & Bulat, I. (2015). Evaluation and Comparison of Anatomical Landmark Detection Methods for Cephalometric X-Ray Images: A Grand Challenge. IEEE Transactions on Medical Imaging. doi:10.1109/TMI.2015.2412951 Peer Reviewed verified by ORBi |
 | Marée, R., Geurts, P., & Wehenkel, L. (2014). Towards generic image classification: an extensive empirical study. (1). ORBi-University of Liège. https://orbi.uliege.be/handle/2268/175525. |
 | Potier, D., Davie, K., Hulselmans, G., Naval Sanchez, M., Haagen, L., Huynh-Thu, V. A., Koldere, D., Celik, A., Geurts, P., Christiaens, V., & Aerts, S. (18 December 2014). Mapping Gene Regulatory Networks in Drosophila Eye Development by Large-Scale Transcriptome Perturbations and Motif Inference. Cell Reports, 9 (6), 2290-2303. doi:10.1016/j.celrep.2014.11.038 Peer Reviewed verified by ORBi |
 | Joly, A., Geurts, P., & Wehenkel, L. (2014). Random forests with random projections of the output space for high dimensional multi-label classification. In Machine Learning and Knowledge Discovery in Databases. doi:10.1007/978-3-662-44848-9_39 Peer reviewed |
 | Sutera, A., Joly, A., François-Lavet, V., Qiu, Z., Louppe, G., Ernst, D., & Geurts, P. (2014). Simple connectome inference from partial correlation statistics in calcium imaging. In J. Soriano, D. Battaglia, I. Guyon, V. Lemaire, J. Orlandi, ... B. Ray (Eds.), Neural Connectomics Challenge. Springer. Peer reviewed |
 | Botta, V., Louppe, G., Geurts, P., & Wehenkel, L. (2014). Exploiting SNP Correlations within Random Forest for Genome-Wide Association Studies. PLoS ONE. doi:10.1371/journal.pone.0093379 Peer Reviewed verified by ORBi |
 | Azrour, S., Pierard, S., Geurts, P., & Van Droogenbroeck, M. (2014). Data normalization and supervised learning to assess the condition of patients with multiple sclerosis based on gait analysis. In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) (pp. 649-654). Peer reviewed |
 | Ruyssinck, J., Huynh-Thu, V. A., Geurts, P., Dhaene, T., Demeester, P., & Saeys, Y. (25 March 2014). NIMEFI: Gene Regulatory Network Inference using Multiple Ensemble Feature Importance Algorithms. PLoS ONE, 9 (3), 92709. doi:10.1371/journal.pone.0092709 Peer Reviewed verified by ORBi |
 | Schepers, K., Mouchet, F., Dirix, V., De Schutter, I., Kersten, J., Verscheure, V., Geurts, P., Mahavir, S., Van Vooren, J.-P., & Mascart, F. (February 2014). Long-incubation time-interferon-gamma release assays in response to PPD-, ESAT-6- and/or CFP-10 for the diagnosis of Mycobacterium tuberculosis infection in children. Clinical and Vaccine Immunology, 21 (2), 111-118. doi:10.1128/CVI.00525-13 Peer Reviewed verified by ORBi |
 | JOSSE, C., Bouznad, N., Geurts, P., Irrthum, A., Huynh-Thu, V. A., Servais, L., Hego, A., Delvenne, P., Bours, V., & Oury, C. (2014). Identification of a microRNA landscape targeting the PI3K/Akt signaling pathway in inflammation-induced colorectal carcinogenesis. American Journal of Physiology - Gastrointestinal and Liver Physiology, 306, 229-43. doi:10.1152/ajpgi.00484.2012 Peer Reviewed verified by ORBi |
 | Marchand, G., Huynh-Thu, V. A., Kane, N. C., Arribat, S., Vares, D., Rengel, D., Balzergue, S., Rieseberg, L. H., Vincourt, P., Geurts, P., Vignes, M., & Langlade, N. B. (2014). Bridging physiological and evolutionary time-scales in a gene regulatory network. New Phytologist, 203 (2), 685-696. doi:10.1111/nph.12818 Peer Reviewed verified by ORBi |
 | Vandaele, R., Marée, R., JODOGNE, S., & Geurts, P. (2014). Automatic Cephalometric X-Ray Landmark Detection Challenge 2014: A tree-based algorithm. ISBI. |
 | Vandaele, R., Marée, R., JODOGNE, S., & Geurts, P. (2014). Automatic Landmark Detection in 2D images: A tree-based approach with multiresolution pixel features. ULG. |
 | Schrynemackers, M., Kuffner, R., & Geurts, P. (03 December 2013). On protocols and measures for the validation of supervised methods for the inference of biological networks. Frontiers in Genetics, 4 (262). doi:10.3389/fgene.2013.00262 Peer Reviewed verified by ORBi |
 | Louppe, G., Wehenkel, L., Sutera, A., & Geurts, P. (2013). Understanding variable importances in forests of randomized trees. In Advances in Neural Information Processing Systems 26. Peer reviewed |
 | Liao, Y., Du, W., Geurts, P., & Leduc, G. (11 October 2013). DMFSGD: A Decentralized Matrix Factorization Algorithm for Network Distance Prediction. IEEE/ACM Transactions on Networking, 21 (5), 1511-1524. doi:10.1109/TNET.2012.2228881 Peer Reviewed verified by ORBi |
 | Mikut, R., Dickmeis, T., Driever, W., Geurts, P., Hamprecht, F. A., Kausler, B. X., Ledesma-Carbayo, M. J., Maree, R., Mikula, K., Pantazis, P., Ronneberger, O., Santos, A., Stotzka, R., Strahle, U., & Peyrieras, N. (August 2013). Automated Processing of Zebrafish Imaging Data: A Survey. Zebrafish, 10 (3), 401-421. doi:10.1089/zeb.2013.0886 Peer reviewed |
 | Marée, R., Wehenkel, L., & Geurts, P. (2013). Extremely Randomized Trees and Random Subwindows for Image Classification, Annotation, and Retrieval. In A. Criminisi & J. Shotton (Eds.), Decision Forests in Computer Vision and Medical Image Analysis, Advances in Computer Vision and Pattern Recognition (pp. 125-142). Springer. Peer reviewed |
 | Huynh-Thu, V. A., Wehenkel, L., & Geurts, P. (2013). Gene regulatory network inference from systems genetics data using tree-based methods. In A. de la Fuente (Ed.), Gene Network Inference - Verification of Methods for Systems Genetics Data (pp. 63-85). Springer. doi:10.1007/978-3-642-45161-4_5 Peer reviewed |
 | Huynh-Thu, V. A., & Geurts, P. (2013). Gene regulatory network inference from expression and genetic data using tree-based methods [Paper presentation]. STATSEQ meeting on Gene Network Inference with Systems genetic data and beyond, Paris, France. Peer reviewed |
 | Du, W., Liao, Y., Geurts, P., & Leduc, G. (2012). Ordinal Rating of Network Performance and Inference by Matrix Completion. (arXiv:1211.0447). |
 | Maes, F., Geurts, P., & Wehenkel, L. (2012). Embedding Monte Carlo search of features in tree-based ensemble methods. In P. Flach, T. De Bie, ... N. Cristianini (Eds.), Machine Learning and Knowledge Discovery in Data Bases (pp. 191-206). Springer. Peer reviewed |
 | Schnitzler, F., Geurts, P., & Wehenkel, L. (2012). Mixtures of Bagged Markov Tree Ensembles. In A. Cano Utrera, M. Gómez-Olmedo, ... T. Nielsen (Eds.), Proceedings of the 6th European Workshop on Probabilistic Graphical Models (pp. 283-290). Peer reviewed |
 | Hiard, S., Geurts, P., & Wehenkel, L. (2012). Comparator selection for RPC with many labels. In ECAI 2012 : 20th European Conference on Artificial Intelligence : 27-31 August 2012, Montpellier, France (pp. 408-413). Amsterdam, Netherlands: IOS Press. doi:10.3233/978-1-61499-098-7-408 Peer reviewed |
 | Marbach, D., Costello, J. C., Küffner, R., Vega, N. M., Prill, R. J., Camacho, D. M., Allison, K. R., Aderhold, A., Bonneau, R., Chen, Y., Cordero, F., Crane, M., Dondelinger, F., Drton, M., Esposito, R., Foygel, R., de la Fuente, A., Gertheiss, J., Geurts, P., ... Stolovitzky, G. (15 July 2012). Wisdom of crowds for robust gene network inference. Nature Methods, 9, 796-804. doi:10.1038/nmeth.2016 Peer Reviewed verified by ORBi |
Huynh-Thu, V. A., Vandel, J., Irrthum, A., Wehenkel, L., & Geurts, P. (01 June 2012). Inferring gene regulatory networks from genetical genomics data [Paper presentation]. Capita Selecta in Complex Disease Analysis, Liege, Belgium. Editorial reviewed |
 | Joly, A., Schnitzler, F., Geurts, P., & Wehenkel, L. (2012). L1-based compression of random forest models. In Proceeding of the 21st Belgian-Dutch Conference on Machine Learning. |
 | Schnitzler, F., Ammar, S., Leray, P., Geurts, P., & Wehenkel, L. (2012). Approximation efficace de mélanges bootstrap d’arbres de Markov pour l’estimation de densité. In L. Bougrain (Ed.), Actes de la 14e Conférence Francophone sur l'Apprentissage Automatique (CAp 2012) (pp. 207-222). Peer reviewed |
 | Huynh-Thu, V. A., Saeys, Y., Wehenkel, L., & Geurts, P. (25 April 2012). Statistical interpretation of machine learning-based feature importance scores for biomarker discovery. Bioinformatics, 28 (13), 1766-1774. doi:10.1093/bioinformatics/bts238 Peer Reviewed verified by ORBi |
 | Joly, A., Schnitzler, F., Geurts, P., & Wehenkel, L. (2012). L1-based compression of random forest models. In 20th European Symposium on Artificial Neural Networks. Peer reviewed |
 | Liao, Y., Du, W., Geurts, P., & Leduc, G. (2012). DMFSGD: A Decentralized Matrix Factorization Algorithm for Network Distance Prediction. (arXiv:1201.1174). |
 | Louppe, G., & Geurts, P. (2012). Ensembles on Random Patches. In Machine Learning and Knowledge Discovery in Databases. Berlin, Germany: Springer-Verlag. Peer reviewed |
 | Jeanray, N., Marée, R., Pruvot, B., Stern, O., Geurts, P., Wehenkel, L., & Muller, M. (08 December 2011). Phenotype Classification of Zebrafish Embryos by Supervised Learning [Poster presentation]. BelTox Annual Scientific Meeting 2011, Mechelen, Belgium. |
 | Liao, Y., Du, W., Geurts, P., & Leduc, G. (2011). Decentralized Prediction of End-to-End Network Performance Classes. In Proc. of the 7th International Conference on emerging Networking EXperiments and Technologies (CoNEXT). ACM. doi:10.1145/2079296.2079310 Peer reviewed |
 | Joly, A., Schnitzler, F., Geurts, P., & Wehenkel, L. (29 November 2011). Pruning randomized trees with L1-norm regularization [Poster presentation]. DYSCO Study Day, Leuven-Heverlee, Belgium. |
 | Jeanray, N., Marée, R., Pruvot, B., Stern, O., Geurts, P., Wehenkel, L., & Muller, M. (02 September 2011). Phenotype Classification of Zebrafish Embryos by Supervised Learning [Paper presentation]. MIAAB 2011 - Microscopic Image Analysis with Applications in Biology, Heidelberg, Germany. |
 | Schnitzler, F., ammar, S., leray, P., Geurts, P., & Wehenkel, L. (2011). Efficiently approximating Markov tree bagging for high-dimensional density estimation. In D. Gunopulos, T. Hofmann, D. Malerba, ... M. Vazirgiannis (Eds.), Machine Learning and Knowledge Discovery in Databases, Part III (pp. 113-128). Berlin, Heidelberg, Germany: Springer-Verlag. doi:10.1007/978-3-642-23808-6_8 Peer reviewed |
 | Levels, J. H., Geurts, P., Karlsson, H., Marée, R., Ljunggren, S., Fornander, L., Wehenkel, L., Lindahl, M., Stroes, E. S., Kuivenhoven, J. A., & Meijers, J. C. (28 June 2011). High-density lipoprotein proteome dynamics in human endotoxemia. Proteome Science, 9 (1), 34. doi:10.1186/1477-5956-9-34 Peer Reviewed verified by ORBi |
 | Jeanray, N., Marée, R., Pruvot, B., Stern, O., Geurts, P., Wehenkel, L., & Muller, M. (20 May 2011). Phenotype Classification of Zebrafish Embryos by Supervised Learning [Poster presentation]. Benelearn 2011 - 20th Annual Belgian Dutch Conference on Machine Learning, La Haye, Netherlands. |
 | Stern, O., Marée, R., Aceto, J., Jeanray, N., Muller, M., Wehenkel, L., & Geurts, P. (20 May 2011). Zebrafish Skeleton Measurements using Image Analysis and Machine Learning Methods [Poster presentation]. Belgian Dutch Conference on Machine learning (Benelearn). |
 | Geurts, P. (April 2011). Learning from positive and unlabeled examples by enforcing statistical significance. Proceedings of Machine Learning Research, 15. Peer Reviewed verified by ORBi |
 | Schnitzler, F., Geurts, P., & Wehenkel, L. (21 March 2011). Looking for applications of mixtures of Markov trees in bioinformatics [Paper presentation]. BioMAGNet Annual Meeting 2011, Bruxelles, Belgium. |
 | Stern, O., Marée, R., Aceto, J., Jeanray, N., Muller, M., Wehenkel, L., & Geurts, P. (2011). Automatic localization of interest points in zebrafish images with tree-based methods. In Proceedings of the 6th IAPR International Conference on Pattern Recognition in Bioinformatics. Springer. Peer reviewed |
 | Geurts, P., & Louppe, G. (January 2011). Learning to rank with extremely randomized trees. Proceedings of Machine Learning Research, 14, 49-61. Peer Reviewed verified by ORBi |
 | Garbacki, N., Di Valentin, E., Huynh-Thu, V. A., Geurts, P., Irrthum, A., Crahay, C., Arnould, T., Deroanne, C., Piette, J., Cataldo, D., & Colige, A. (2011). MicroRNAs Profiling in Murine Models of Acute and Chronic Asthma: A Relationship with mRNAs Targets. PLoS ONE. doi:10.1371/journal.pone.0016509 Peer Reviewed verified by ORBi |
Huynh-Thu, V. A., Saeys, Y., Wehenkel, L., & Geurts, P. (2011). Statistical interpretation of machine learning-based feature rankings for biomarker discovery [Paper presentation]. Benelux Bioinformatics Conference (BBC11), Luxembourg, Luxembourg. Peer reviewed |
Huynh-Thu, V. A., Irrthum, A., Wehenkel, L., Saeys, Y., & Geurts, P. (2011). Inferring gene regulatory networks from expression data using tree-based methods [Paper presentation]. ISMB/ECCB 2011, Vienna, Austria. Peer reviewed |
Louppe, G., & Geurts, P. (11 December 2010). A zealous parallel gradient descent algorithm [Poster presentation]. NIPS 2010 Workshop on Learning on Cores, Clusters and Clouds, Whistler, Canada. |
Huynh-Thu, V. A., Irrthum, A., Wehenkel, L., Saeys, Y., & Geurts, P. (16 November 2010). Regulatory network inference with GENIE3: application to the DREAM5 challenge [Paper presentation]. 3rd Annual Joint Conference on Systems Biology, Regulatory Genomics, and Reverse Engineering Challenges, New York City, United States - New York. Editorial reviewed |
 | Huynh-Thu, V. A., Irrthum, A., Wehenkel, L., & Geurts, P. (October 2010). Inferring Regulatory Networks from Expression Data using Tree-based Methods [Paper presentation]. Workshop on Machine Learning in Systems Biology (MLSB10), Edinburgh, United Kingdom. Peer reviewed |
 | Huynh-Thu, V. A., Irrthum, A., Wehenkel, L., & Geurts, P. (28 September 2010). Inferring Regulatory Networks from Expression Data Using Tree-Based Methods. PLoS ONE, 5 (9), 12776. doi:10.1371/journal.pone.0012776 Peer Reviewed verified by ORBi |
 | Huynh-Thu, V. A., Irrthum, A., Wehenkel, L., & Geurts, P. (September 2010). Inferring regulatory networks from expression data using tree-based methods [Poster presentation]. ECCB 2010, Ghent, Belgium. Peer reviewed |
 | Liao, Y., Geurts, P., & Leduc, G. (11 May 2010). Network Distance Prediction Based on Decentralized Matrix Factorization. Lecture Notes in Computer Science, 6091, 15-26. doi:10.1007/978-3-642-12963-6_2 Peer reviewed |
 | Marée, R., Denis, P., Wehenkel, L., & Geurts, P. (2010). Incremental Indexing and Distributed Image Search using Shared Randomized Vocabularies. In ACM Proceedings MIR 2010. doi:10.1145/1743384.1743405 Peer reviewed |
 | El Khayat, I., Geurts, P., & Leduc, G. (February 2010). Enhancement of TCP over wired/wireless networks with packet loss classifiers inferred by supervised learning. Wireless Networks, 16 (2), 273-290. doi:10.1007/s11276-008-0129-y Peer Reviewed verified by ORBi |
 | Marée, R., Stern, O., & Geurts, P. (2010). Biomedical Imaging Modality Classification Using Bags of Visual and Textual Terms with Extremely Randomized Trees: Report of ImageCLEF 2010 Experiments. In CLEF 2010: Padua, Italy - Notebook Papers/LABs/Workshops. |
 | De Lobel, L., Geurts, P., Baele, G., Castro-Giner, F., Kogevinas, M., & Van Steen, K. (2010). A screening methodology based on Random Forests to improve the detection of gene-gene interactions. European Journal of Human Genetics, 18 (1127), 1132. doi:10.1038/ejhg.2010.48 Peer Reviewed verified by ORBi |
 | Cornélusse, B., Geurts, P., & Wehenkel, L. (12 December 2009). Tree based ensemble models regularization by convex optimization [Paper presentation]. NIPS-09 workshop on Optimization for Machine Learning, Whistler, Canada. |
 | Geurts, P., Irrthum, A., & Wehenkel, L. (December 2009). Supervised learning with decision tree-based methods in computational and systems biology. Molecular Biosystems, 5 (12), 1593-1605. doi:10.1039/b907946g Peer Reviewed verified by ORBi |
 | Huynh-Thu, V. A., Irrthum, A., Wehenkel, L., & Geurts, P. (September 2009). Inferring regulatory networks from expression data using tree-based methods [Poster presentation]. Benelux Bioinformatics Conference (BBC09), Liège, Belgium. Peer reviewed |
 | Liao, Y., Kaafar, M. A., Gueye, B., Cantin, F., Geurts, P., & Leduc, G. (12 May 2009). Detecting Triangle Inequality Violations in Internet Coordinate Systems by Supervised Learning. Lecture Notes in Computer Science, 5550, 352-363. doi:10.1007/978-3-642-01399-7_28 Peer reviewed |
 | Dumont, M., Marée, R., Wehenkel, L., & Geurts, P. (2009). Fast Multi-Class Image Annotation with Random Subwindows and Multiple Output Randomized Trees. In Proc. International Conference on Computer Vision Theory and Applications (VISAPP) (pp. 196-203). Peer reviewed |
 | Marée, R., Geurts, P., & Wehenkel, L. (30 January 2009). Content-based Image Retrieval by Indexing Random Subwindows with Randomized Trees. IPSJ Transactions on Computer Vision and Applications, 1. doi:10.2197/ipsjtcva.1.46 Peer Reviewed verified by ORBi |
 | De Seny, D., Ribbens, C., Cobraiville, G., Meuwis, M.-A., Marée, R., Geurts, P., Wehenkel, L., Louis, E., Merville, M.-P., Fillet, M., & Malaise, M. (2009). Protéomique par SELDI-TOF-MS des maladies inflammatoires articulaires: identification des protéines S100 comme protéines d'intérêt. Revue Médicale de Liège, 64 (Spec No), 29-35. Peer reviewed |
 | Marée, R., Stevens, B., Geurts, P., Guern, Y., & Mack, P. (2009). A Machine Learning Approach for Material Detection in Hyperspectral Images. In Proc. 6th IEEE Workshop on Object Tracking and Classification Beyond and in the Visible Spectrum (OTCBVS-CVPR09). IEEE. doi:10.1109/CVPR.2009.5204119 Peer reviewed |
Huynh-Thu, V. A., Irrthum, A., Wehenkel, L., & Geurts, P. (2009). Inferring regulatory networks from expression data using tree-based methods [Paper presentation]. 2009 joint conference on Systems Biology, Regulatory Genomics, and Reverse Engineering Challenges, Boston, United States - Massachusetts. Editorial reviewed |
 | Huynh-Thu, V. A., Wehenkel, L., & Geurts, P. (December 2008). Exploiting tree-based variable importances to selectively identify relevant variables [Poster presentation]. Benelux Bioinformatics Conference (BBC08), Maastricht, Netherlands. Peer reviewed |
 | Botta, V., Hansoul, S., Geurts, P., & Wehenkel, L. (2008). Raw genotypes vs haplotype blocks for genome wide association studies by random forests. In Proc. of MLSB 2008, second workshop on Machine Learning in Systems Biology. Peer reviewed |
 | Botta, V., Geurts, P., Hansoul, S., & Wehenkel, L. (May 2008). Prediction of genetic risk of complex diseases by supervised learning [Paper presentation]. Benelearn: The annual machine learning conference of Belgium and The Netherlands. |
 | Huynh-Thu, V. A., Wehenkel, L., & Geurts, P. (May 2008). Deriving p-values for tree-based variable importance measures [Paper presentation]. Benelearn, Spa, Belgium. Peer reviewed |
 | Huynh-Thu, V. A., Wehenkel, L., & Geurts, P. (2008). Exploiting tree-based variable importances to selectively identify relevant variables [Paper presentation]. Third Workshop on New Challenges for Feature Selection in Data Mining and Knowledge Discovery (FSDM 2008). Peer reviewed |
 | Meuwis, M.-A.* , Fillet, M.* , Lutteri, L., Marée, R., Geurts, P., De Seny, D., Malaise, M., Chapelle, J.-P., Wehenkel, L., Belaiche, J., Merville, M.-P., & Louis, E. (2008). Proteomics for prediction and characterization of response to infliximab in Crohn's disease: a pilot study. Clinical Biochemistry, 41 (12), 960-7. doi:10.1016/j.clinbiochem.2008.04.021 Peer Reviewed verified by ORBi* These authors have contributed equally to this work. |
 | Huynh-Thu, V. A., Wehenkel, L., & Geurts, P. (2008). Exploiting tree-based variable importances to selectively identify relevant variables. Proceedings of Machine Learning Research, 4, 60-73. Peer Reviewed verified by ORBi |
Levels, J. H., Marée, R., Geurts, P., Kuivenhoven, J. A., Wehenkel, L., Kastelein, J. J., & Meijers, J. C. M. (2008). Compositional protein analysis of HDL by SELDI-TOF MS during experimental endotoxemia [Poster presentation]. 77th European Atherosclerosis Society Congress, Istanbul, Turkey. |
 | Del Angel, A., Geurts, P., Ernst, D., Glavic, M., & Wehenkel, L. (October 2007). Estimation of rotor angles of synchronous machines using artificial neural networks and local PMU-based quantities. Neurocomputing, 70 (16-18), 2668-2678. doi:10.1016/j.neucom.2006.12.017 Peer Reviewed verified by ORBi |
 | Huynh-Thu, V. A., Hiard, S., Geurts, P., Muller, M., Struman, I., Martial, J., & Wehenkel, L. (September 2007). Detection of micro-RNA/gene interactions involved in angiogenesis using machine learning techniques [Poster presentation]. Workshop on Machine Learning in Systems Biology (MLSB07), Evry, France. Peer reviewed |
Marée, R., Dumont, M., Geurts, P., & Wehenkel, L. (22 July 2007). Random Subwindows and Randomized Trees for Image Retrieval, Classification, and Annotation [Poster presentation]. 15th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB) and 6th European Conference on Computational Biology (ECCB), Vienna, Austria. |
 | El Khayat, I., Geurts, P., & Leduc, G. (July 2007). Machine-learnt versus analytical models of TCP throughput. Computer Networks, 51 (10), 2631-2644. doi:10.1016/j.comnet.2006.11.017 Peer Reviewed verified by ORBi |
 | Geurts, P., Touleimat, N., Dutreix, M., & d'Alche-Buc, F. (03 May 2007). Inferring biological networks with output kernel trees. BMC Bioinformatics, 8 (Suppl. 2), 4. doi:10.1186/1471-2105-8-S2-S4 Peer Reviewed verified by ORBi |
Dumont, M., Marée, R., Geurts, P., & Wehenkel, L. (2007). Random Subwindows and Multiple Output Decision Trees for Generic Image Annotation [Poster presentation]. 16th Annual Machine Learning Conference of Belgium and The Netherlands, Amsterdam, Netherlands. |
 | Geurts, P., Wehenkel, L., & d'Alché-Buc, F. (2007). Gradient boosting for kernelized output spaces. In Proceedings of the 24th International Conference on Machine Learning (pp. 289-296). doi:10.1145/1273496.1273533 Peer reviewed |
 | Marée, R., Geurts, P., & Wehenkel, L. (2007). Content-based Image Retrieval by Indexing Random Subwindows with Randomized Trees. In Proc. 8th Asian Conference on Computer Vision (ACCV), LNCS (pp. 611-620). Springer-Verlag. Peer reviewed |
 | Marée, R., Geurts, P., & Wehenkel, L. (2007). Random subwindows and extremely randomized trees for image classification in cell biology. BMC Cell Biology, 8 (Suppl. 1). doi:10.1186/1471-2121-8-S1-S2 Peer Reviewed verified by ORBi |
 | Meuwis, M.-A.* , Fillet, M.* , Geurts, P., De Seny, D., Lutteri, L., Chapelle, J.-P., Bours, V., Wehenkel, L., Belaiche, J., Malaise, M., Louis, E., & Merville, M.-P. (2007). Biomarker discovery for inflammatory bowel disease, using proteomic serum profiling. Biochemical Pharmacology, 73 (9), 1422-1433. doi:10.1016/j.bcp.2006.12.019 Peer Reviewed verified by ORBi* These authors have contributed equally to this work. |
 | El Khayat, I., Geurts, P., & Leduc, G. (May 2006). On the accuracy of analytical models of TCP throughput. Lecture Notes in Computer Science, 3976, 488-500. doi:10.1007/11753810_41 Peer reviewed |
 | Geurts, P., Ernst, D., & Wehenkel, L. (April 2006). Extremely randomized trees. Machine Learning, 63 (1), 3-42. doi:10.1007/s10994-006-6226-1 Peer Reviewed verified by ORBi |
 | Geurts, P., Wehenkel, L., & d Alché-Buc, F. (2006). Kernelizing the output of tree-based methods. In Proceedings of the 23rd International Conference on Machine Learning (pp. 345-352). Acm. Peer reviewed |
 | Geurts, P., Wehenkel, L., & d'Alché-Buc, F. (2006). OK3: Méthode d’arbres à sortie noyau pour la prédiction de sorties structurées et l’apprentissage de noyau. In Proc. of CAP (Conférence francophone d'apprentissage) (pp. 16). Peer reviewed |
 | Quach, M., Geurts, P., & d Alché-Buc, F. (2006). Elucidating the structure of genetic regulatory networks: a study of a second order dynamical model on artificial data. In Proc. of the 14th European Symposium on Artificial Neural Networks. Peer reviewed |
 | Geurts, P., Touleimat, N., Dutreix, M., & d Alché-Buc, F. (2006). Completion of biological networks: the output kernel trees approach. In Proceedings of the the Workshop on Probabilistic Modeling and Machine Learning in Structural and Systems Biology. Peer reviewed |
 | Wehenkel, L., Glavic, M., Geurts, P., & Ernst, D. (2006). Automatic learning of sequential decision strategies for dynamic security assessment and control. In Proceedings of the IEEE Power Engineering Society General Meeting 2006. doi:10.1109/PES.2006.1708874 Peer reviewed |
 | Marée, R., Geurts, P., & Wehenkel, L. (2006). Biological Image Classification with Random Subwindows and Extra-Trees [Paper presentation]. Bio-Image Informatics (Workshop on Multiscale Biological Imaging, Data Mining & Informatics), Santa Barbara, United States. |
 | Wehenkel, L., Ernst, D., & Geurts, P. (2006). Ensembles of extremely randomized trees and some generic applications. In Proceedings of Robust Methods for Power System State Estimation and Load Forecasting. Peer reviewed |
 | Auvray, V., Geurts, P., & Wehenkel, L. (2006). A Semi-Algebraic Description of Discrete Naive Bayes Models with Two Hidden Classes. In Proc. Ninth International Symposium on Artificial Intelligence and Mathematics. Peer reviewed |
 | Geurts, P., Marée, R., & Wehenkel, L. (2006). Segment and combine: a generic approach for supervised learning of invariant classifiers from topologically structured data. In Proceedings of the Machine Learning Conference of Belgium and The Netherlands (Benelearn) (pp. 15-23). Peer reviewed |
 | Wehenkel, L., Glavic, M., Geurts, P., & Ernst, D. (2006). About automatic learning for advanced sensing, monitoring and control of electric power systems. In Proceedings of the Second Carnegie Mellon Conference in Electric Power Systems: Monitoring, Sensing, Software and its Valuation for the Changing electric Power Industry. Peer reviewed |
 | El Khayat, I., Geurts, P., & Leduc, G. (May 2005). Improving TCP in wireless networks with an adaptive machine-learnt classifier of packet loss causes. Lecture Notes in Computer Science, 3462, 549-560. doi:10.1007/11422778_44 Peer reviewed |
 | Ernst, D., Geurts, P., & Wehenkel, L. (April 2005). Tree-based batch mode reinforcement learning. Journal of Machine Learning Research, 6, 503-556. Peer Reviewed verified by ORBi |
 | Ernst, D., Glavic, M., Geurts, P., & Wehenkel, L. (2005). Approximate value iteration in the reinforcement learning context. Application to electrical power system control. International Journal of Emerging Electrical Power Systems, 3 (1). doi:10.2202/1553-779X.1066 Peer Reviewed verified by ORBi |
 | Marée, R., Geurts, P., Piater, J., & Wehenkel, L. (2005). Decision Trees and Random Subwindows for Object Recognition. In ICML workshop on Machine Learning Techniques for Processing Multimedia Content (MLMM2005). Peer reviewed |
 | Geurts, P. (2005). Bias vs. variance decomposition for regression and classification. In O. Maimon & L. Rokach (Eds.), Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers. Kluwer Academic Publishers. |
 | Geurts, P., Blanco Cuesta, A., & Wehenkel, L. (2005). Segment and combine approach for Biological Sequence Classification. In Proc. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2005) (pp. 194-201). doi:10.1109/cibcb.2005.1594917 Peer reviewed |
 | Geurts, P., & Wehenkel, L. (2005). Segment and combine approach for non-parametric time-series classification. Lecture Notes in Computer Science, 3721, 478-485. doi:10.1007/11564126_48 Peer reviewed |
 | De Seny, D.* , Fillet, M.* , Meuwis, M.-A., Geurts, P., Lutteri, L., Ribbens, C., Bours, V., Wehenkel, L., Piette, J., Malaise, M., & Merville, M.-P. (2005). Discovery of new rheumatoid arthritis biomarkers using the surface-enhanced laser desorption/ionization time-of-flight mass spectrometry ProteinChip approach. Arthritis and Rheumatism, 52 (12), 3801-12. doi:10.1002/art.21607 Peer Reviewed verified by ORBi* These authors have contributed equally to this work. |
 | Geurts, P., & Wehenkel, L. (2005). Closed-form dual perturb and combine for tree-based models. In Proceedings of the International Conference on Machine Learning (ICML 2005). doi:10.1145/1102351.1102381 Peer reviewed |
 | Geurts, P., Fillet, M., De Seny, D., Meuwis, M.-A., Malaise, M., Merville, M.-P., & Wehenkel, L. (2005). Proteomic mass spectra classification using decision tree based ensemble methods. Bioinformatics, 21 (14), 3138-45. doi:10.1093/bioinformatics/bti494 Peer Reviewed verified by ORBi |
 | Marée, R., Geurts, P., Piater, J., & Wehenkel, L. (2005). Random Subwindows for Robust Image Classification. In C. Schmid, S. Soatto, ... C. Tomasi (Eds.), Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2005) (pp. 34-40). Peer reviewed |
 | Marée, R., Geurts, P., Piater, J., & Wehenkel, L. (2005). Biomedical image classification with random subwindows and decision trees. In Computer Vision for Biomedical Image Applications (pp. 220-229). Berlin, Germany: Springer-Verlag Berlin. doi:10.1007/11569541_23 Peer reviewed |
 | Geurts, P., El Khayat, I., & Leduc, G. (2004). A Machine Learning Approach to Improve Congestion Control over Wireless Computer Networks [Paper presentation]. ICDM 2004, Brighton, United Kingdom. doi:10.1109/ICDM.2004.10063 Peer reviewed |
De Seny, D., Fillet, M., Meuwis, M.-A., Lutteri, L., Geurts, P., Wehenkel, L., Bours, V., Piette, J., Malaise, M., & Merville, M.-P. (September 2004). Discovery of new rheumatoid arthritis biomarkers using SELDI-TOF-MS ProteinChip approach. Arthritis and Rheumatism, 50 (9, Suppl. S), 124. |
 | Marée, R., Geurts, P., Piater, J., & Wehenkel, L. (2004). A generic approach for image classification based on decision tree ensembles and local sub-windows. In K.-S. Hong & Z. Zhang (Eds.), Proceedings of the 6th Asian Conference on Computer Vision (pp. 860-865). Asian Federation of Computer Vision Societies (AFCV). Peer reviewed |
 | Geurts, P. (2003). Traitement de données volumineuses par ensemble d'arbres aléatoires. Revue des nouvelles technologies de l'information, Numéro spécial entreposage et fouille de données, 1, 111-122. Peer reviewed |
 | Marée, R., Geurts, P., & Wehenkel, L. (2003). Une méthode générique pour la classification automatique d'images à partir des pixels. Revue des Nouvelles Technologies de l'Information, 1, 227-238. Peer reviewed |
 | Ernst, D., Geurts, P., & Wehenkel, L. (2003). Iteratively extending time horizon reinforcement learning. In Machine Learning: ECML 2003, 14th European Conference on Machine Learning (pp. 96-107). Berlin, Germany: Springer-Verlag Berlin. doi:10.1007/978-3-540-39857-8_11 Peer reviewed |
 | Marée, R., Geurts, P., Visimberga, G., Piater, J., & Wehenkel, L. (2003). An empirical comparison of machine learning algorithms for generic image classification. In F. Coenen, A. Preece, ... A. L. Macintosh (Eds.), Proceedings of the 23rd SGAI international conference on innovative techniques and applications of artificial intelligence, Research and development in intelligent systems XX (pp. 169-182). Springer. Peer reviewed |
 | Geurts, P. (2002). Contributions to decision tree induction: bias/variance tradeoff and time series classification [Doctoral thesis, ULiège - Université de Liège]. ORBi-University of Liège. https://orbi.uliege.be/handle/2268/25737 |
 | Geurts, P. (2001). Dual Perturb and Combine Algorithm. In Proceedings of AISTATS 2001, Eighth International Workshop on Artificial Intelligence and Statistics (pp. 196-201). Peer reviewed |
 | Geurts, P. (2001). Pattern extraction for time-series classification. In Proceedings of PKDD 2001, 5th European Conference on Principles of Data Mining and Knowledge Discovery (pp. 115-127). Springer-Verlag. Peer reviewed |
 | Geurts, P., Olaru, C., & Wehenkel, L. (2001). Improving the bias/variance tradeoff of decision trees - towards soft tree induction. Engineering intelligent systems, 9, 195-204. Peer reviewed |
 | Geurts, P. (2000). Some enhancements of decision tree bagging. In Proceedings of PKDD 2000, 4th European Conference on Principles of Data Mining and Knowledge Discovery (pp. 136-147). Lyon, France: Springer-Verlag. Peer reviewed |
 | Geurts, P., & Wehenkel, L. (2000). Temporal machine learning for switching control. In Proceedings of PKDD 2000, 4th European Conference on Principles of Data Mining and Knowledge Discovery (pp. 401-408). Lyon, France: Springer-Verlag. Peer reviewed |
 | Geurts, P., & Wehenkel, L. (2000). Investigation and reduction of discretization Variance in decision tree induction. In Proceedings of ECML 2000, European Conference on Machine Learning (pp. 162-170). Springer-Verlag. Peer reviewed |
 | Olaru, C., Geurts, P., & Wehenkel, L. (1999). Data mining tools and application in power system engineering. In Proceedings of the 13th Power System Computation Conference, PSCC99 (pp. 324-330). Peer reviewed |
 | Geurts, P., & Wehenkel, L. (1998). Early prediction of electric power system blackouts by temporal machine learning. In Proceedings of ICML-AAAI 98 Workshop on "Predicting the future: AI approaches to time series analysis" (pp. 21-27). Peer reviewed |
 | Geurts, P., & Wehenkel, L. (1998). Visualizing dynamic power system scenarios for data mining. In Proceedings of LESCOPE 98, Large Engineering Syst. Conf. on Power Engineering (pp. 217-224). Peer reviewed |
