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2020 – today
- 2026
[j38]Ariel Basso Madjoukeng, Jérôme Fink, Pierre Poitier, Edith Belise Kenmogne, Benoît Frénay:
SSL-SLR: Self-Supervised Representation Learning for Sign Language Recognition. Trans. Mach. Learn. Res. 2026 (2026)- [j37]Armielle Noulapeu Ngaffo
, Julien Albert, Benoît Frénay, Gilles Perrouin:
A Fair Enhanced Bayesian Personalized Ranking Using Adversarial Learning. Trans. Recomm. Syst. 4(2): 31:1-31:28 (2026) - [c61]Pierre Poitier, Loïc Brangier, Ariel Basso Madjoukeng, Benoît Frénay:
Movements as Images: CNNs are Good Feature Extractors in Sign Language Recognition. ESANN 2026 - [c60]Noémie Draguet, Benoît Frénay:
T-SE : A Method Built on Squeeze-and-Excitation Mechanisms for Convolutional Neural Networks' Energy Efficiency. IDA 2026: 45-57 - [c59]Julien Albert, Jens Leysen, Benoît Frénay, Bruno Dumas, Lien Michiels:
Are We Studying the Same Filter Bubble? A Conceptual Framework of the Empirical Literature. UMAP Workshops 2026: 48-63 - 2025
- [j36]Ariel Basso Madjoukeng, Edith Belise Kenmogne, Benoît Frénay:
Robust sparse k-means clustering against STOF observations. Int. J. Data Sci. Anal. 20(8): 6973-6983 (2025) - [j35]Arnaud Bougaham, Valentin Delchevalerie, Mohammed El Adoui, Benoît Frénay:
Industrial and medical anomaly detection through cycle-consistent adversarial networks. Neurocomputing 614: 128762 (2025) - [j34]Robin Ghyselinck, Valentin Delchevalerie, Bruno Dumas, Benoît Frénay:
On the effectiveness of Rotation-Equivariance in U-Net: A Benchmark for Image Segmentation. Trans. Mach. Learn. Res. 2025 (2025) - [c58]Noémie Draguet, Benoît Frénay:
Making Convolutional Neural Networks Energy-Efficient: An Introduction. ESANN 2025 - [c57]Robin Ghyselinck, Valentin Delchevalerie, Benoît Frénay, Bruno Dumas:
Mask-Aware Cropping: Mitigating Mask Imbalance in Segmentation Tasks. ESANN 2025 - [c56]Ariel Basso Madjoukeng, Jérôme Fink, Pierre Poitier, Edith Belise Kenmogne, Benoît Frénay:
Benchmarking Data Augmentation for Contrastive Learning in Static Sign Language Recognition. ESANN 2025 - [c55]Ariel Basso Madjoukeng, Edith Belise Kenmogne, Pierre Poitier, Benoît Frénay, Jérôme Fink:
Local-Global Data Augmentation for Contrastive Learning in Static Sign Language Recognition. IDA 2025: 54-66 - [c54]Robin Ghyselinck, Thibaut Septon, Nicolas Geuens, Margaux Mannaerts, Julie Vijghen, Samy Lachkar, Benoît Frénay, Bruno Dumas:
Computer Assisted Diagnosis: does it make sense for Lungs Tumor Detection? A Qualitative study with Pulmonologists. IHM 2025: 6:1-6:10 - [i13]Arnaud Bougaham, Benoît Frénay:
Towards a Trustworthy Anomaly Detection for Critical Applications through Approximated Partial AUC Loss. CoRR abs/2502.11570 (2025) - [i12]Ariel Basso Madjoukeng, Jérôme Fink, Pierre Poitier, Edith Belise Kenmogne, Benoît Frénay:
SL-SLR: Self-Supervised Representation Learning for Sign Language Recognition. CoRR abs/2509.05188 (2025) - [i11]Valentin Delchevalerie, Nicolas Roy, Arnaud Bougaham, Alexandre Mayer, Benoît Frénay, Michaël Lobet:
Towards Photonic Band Diagram Generation with Transformer-Latent Diffusion Models. CoRR abs/2510.01749 (2025) - 2024
- [j33]Sacha Corbugy, Rebecca Marion, Benoît Frénay:
Gradient-based explanation for non-linear non-parametric dimensionality reduction. Data Min. Knowl. Discov. 38(6): 3690-3718 (2024) - [j32]Pierre Poitier, Jérôme Fink, Benoît Frénay:
Towards better transition modeling in recurrent neural networks: The case of sign language tokenization. Neurocomputing 567: 127018 (2024) - [j31]Arnaud Bougaham, Mohammed El Adoui, Isabelle Linden, Benoît Frénay:
Composite score for anomaly detection in imbalanced real-world industrial dataset. Mach. Learn. 113(7): 4381-4406 (2024) - [j30]Abiola Paterne Chokki, Charalampos Alexopoulos, Ricardo Matheus, Stuti Saxena, Benoît Frénay, Benoît Vanderose:
Do open government data (OGD) portals show signs of knowledge management (KM) practices?: an empirical investigation. Technol. Anal. Strateg. Manag. 36(12): 4829-4844 (2024) - [j29]Rebecca Marion, Benoît Frénay:
Improving the Feature Selection Stability of the Delta Test in Regression. IEEE Trans. Artif. Intell. 5(5): 1911-1917 (2024) - [j28]Mohammed El Adoui, Thomas Herpoel, Benoît Frénay:
Constrained Tiny Machine Learning for Predicting Gas Concentration with I4.0 Low-cost Sensors. ACM Trans. Embed. Comput. Syst. 23(3): 51:1-51:23 (2024) - [c53]Robin Ghyselinck, Valentin Delchevalerie, Pierre Poitier, Benoît Frénay, Bruno Dumas:
Deep Learning for in vivo Bronchial Carinae Detection in Flexible Bronchoscopy. ECAI 2024: 1527-1534 - [c52]Tassadit Bouadi, Benoît Frénay, Luis Galárraga, Pierre Geurts, Barbara Hammer, Gilles Perrouin:
Trust in Artificial Intelligence: Beyond Interpretability. ESANN 2024 - [c51]Abiola Paterne Chokki, Benoît Frénay:
ChatDT: Simplifying Constraint Integration in Decision Trees. ESANN 2024 - [c50]Sacha Corbugy, Thibaut Septon, Bruno Dumas, Benoît Frénay:
Insight-SNE: Understanding t-SNE Embeddings through Interactive Explanation. ESANN 2024 - [c49]Valentin Delchevalerie, Benoît Frénay, Alexandre Mayer:
From Three to Two Dimensions: 2D Quaternion Convolutions for 3D Images. ESANN 2024 - [c48]Robin Ghyselinck, Jérôme Fink, Bruno Dumas, Benoît Frénay:
Leveraging endoscopic data with Contrastive Learning for Crohn's disease detection. ESANN 2024 - [c47]Simon Lejoly, Arianna Piccialli, Arnaud Mahieux, Ann-Carine Vandaele, Benoît Frénay:
Extrapolating Venusian Atmospheric Profiles using MAGMA Gaussian Processes. ESANN 2024 - [c46]Ariel Basso Madjoukeng, Edith Belise Kenmogne, Benoît Frénay:
Fast k-means with Stable Instance Sets. IJCNN 2024: 1-8 - [i10]Robin Ghyselinck, Valentin Delchevalerie, Bruno Dumas, Benoît Frénay:
On the effectiveness of Rotation-Equivariance in U-Net: A Benchmark for Image Segmentation. CoRR abs/2412.09182 (2024) - 2023
- [j27]Hendrik Blockeel, Laurens Devos, Benoît Frénay, Géraldin Nanfack, Siegfried Nijssen:
Decision trees: from efficient prediction to responsible AI. Frontiers Artif. Intell. 6 (2023) - [j26]Adrien Bibal, Valentin Delchevalerie, Benoît Frénay:
DT-SNE: t-SNE discrete visualizations as decision tree structures. Neurocomputing 529: 101-112 (2023) - [j25]Géraldin Nanfack, Paul Temple, Benoît Frénay:
Learning Customised Decision Trees for Domain-knowledge Constraints. Pattern Recognit. 142: 109610 (2023) - [j24]Cristina Morariu, Adrien Bibal, René Cutura, Benoît Frénay, Michael Sedlmair:
Predicting User Preferences of Dimensionality Reduction Embedding Quality. IEEE Trans. Vis. Comput. Graph. 29(1): 745-755 (2023) - [c45]Jérôme Fink, Mathieu De Coster, Joni Dambre, Benoît Frénay:
Trends and Challenges for Sign Language Recognition with Machine Learning. ESANN 2023 - [c44]Benoît Frénay:
A Counterexample to Ockham's Razor and the Curse of Dimensionality: Marginalising Complexity and Dimensionality for GMMs. ESANN 2023 - [c43]Armielle Noulapeu Ngaffo, Julien Albert, Benoît Frénay, Gilles Perrouin:
FairBayRank: A Fair Personalized Bayesian Ranker. ESANN 2023 - [c42]Jérôme Fink, Pierre Poitier, Maxime André, Loup Meurice, Benoît Frénay, Anthony Cleve, Bruno Dumas, Laurence Meurant:
Sign Language-to-Text Dictionary with Lightweight Transformer Models. IJCAI 2023: 5968-5976 - [c41]Sédrick Stassin, Alexandre Englebert, Géraldin Nanfack, Julien Albert, Nassim Versbraegen, Gilles Peiffer, Miriam Doh, Nicolas Riche, Benoît Frénay, Christophe De Vleeschouwer:
An Experimental Investigation into the Evaluation of Explainability Methods for Computer Vision. PKDD/ECML Workshops (1) 2023: 91-106 - [i9]Arnaud Bougaham, Valentin Delchevalerie, Mohammed El Adoui, Benoît Frénay:
Industrial and Medical Anomaly Detection Through Cycle-Consistent Adversarial Networks. CoRR abs/2302.05154 (2023) - [i8]Valentin Delchevalerie, Alexandre Mayer, Adrien Bibal, Benoît Frénay:
SO(2) and O(2) Equivariance in Image Recognition with Bessel-Convolutional Neural Networks. CoRR abs/2304.09214 (2023) - [i7]Sédrick Stassin, Alexandre Englebert, Géraldin Nanfack, Julien Albert, Nassim Versbraegen, Gilles Peiffer, Miriam Doh, Nicolas Riche, Benoît Frénay, Christophe De Vleeschouwer:
An Experimental Investigation into the Evaluation of Explainability Methods. CoRR abs/2305.16361 (2023) - 2022
- [j23]Géraldin Nanfack, Paul Temple, Benoît Frénay:
Constraint Enforcement on Decision Trees: A Survey. ACM Comput. Surv. 54(10s): 201:1-201:36 (2022) - [j22]Abiola Paterne Chokki, Anthony Simonofski, Benoît Frénay, Benoît Vanderose:
Engaging Citizens with Open Government Data: The Value of Dashboards Compared to Individual Visualizations. Digit. Gov. Res. Pract. 3(3): 21:1-21:20 (2022) - [j21]Thibaut Capuano, Houari A. Sahraoui, Benoît Frénay, Benoît Vanderose:
Learning from Code Repositories to Recommend Model Classes. J. Object Technol. 21(3): 3:1-11 (2022) - [j20]Viet Minh Vu, Adrien Bibal, Benoît Frénay:
Integrating Constraints Into Dimensionality Reduction for Visualization: A Survey. IEEE Trans. Artif. Intell. 3(6): 944-962 (2022) - [c40]Abiola Paterne Chokki, Benoît Frénay, Benoît Vanderose:
Open Data Explorer: An End-to-end Tool for Data Storytelling using Open Data. AMCIS 2022 - [c39]Adrien Bibal, Tassadit Bouadi, Benoît Frénay, Luis Galárraga, José Oramas:
AIMLAI: Advances in Interpretable Machine Learning and Artificial Intelligence. CIKM 2022: 5160 - [c38]Abiola Paterne Chokki, Rabeb Abida, Benoît Frénay, Benoît Vanderose, Anthony Cleve:
ODSAG: Enhancing Open Data Discoverability and Understanding through Semantic Annotation (short paper). EGOV-CeDEM-ePart-* 2022 - [c37]Abiola Paterne Chokki, Anthony Simonofski, Antoine Clarinval, Benoît Frénay, Benoît Vanderose:
Fostering Interaction Between Open Government Data Stakeholders: An Exchange Platform for Citizens, Developers and Publishers. EGOV 2022: 228-243 - [c36]Pierre Poitier, Jérôme Fink, Benoît Frénay:
Towards Better Transition Modeling in Recurrent Neural Networks: the Case of Sign Language Tokenization. ESANN 2022 - [c35]Abiola Paterne Chokki, Anthony Simonofski, Benoît Frénay, Benoît Vanderose:
Increasing Awareness and Usefulness of Open Government Data: An Empirical Analysis of Communication Methods. RCIS 2022: 678-684 - [i6]Arnaud Bougaham, Mohammed El Adoui, Isabelle Linden, Benoît Frénay:
Composite Score for Anomaly Detection in Imbalanced Real-World Industrial Dataset. CoRR abs/2211.15513 (2022) - 2021
- [j19]Adrien Bibal, Michael Lognoul, Alexandre de Streel, Benoît Frénay:
Legal requirements on explainability in machine learning. Artif. Intell. Law 29(2): 149-169 (2021) - [j18]Adrien Bibal, Antoine Clarinval, Bruno Dumas, Benoît Frénay:
IXVC: An interactive pipeline for explaining visual clusters in dimensionality reduction visualizations with decision trees. Array 11: 100080 (2021) - [j17]Adrien Bibal, Rebecca Marion, Rainer von Sachs, Benoît Frénay:
BIOT: Explaining multidimensional nonlinear MDS embeddings using the Best Interpretable Orthogonal Transformation. Neurocomputing 453: 109-118 (2021) - [j16]Alexandra Degeest, Benoît Frénay, Michel Verleysen:
Reading grid for feature selection relevance criteria in regression. Pattern Recognit. Lett. 148: 92-99 (2021) - [j15]Pieter Delobelle, Paul Temple, Gilles Perrouin, Benoît Frénay, Patrick Heymans, Bettina Berendt:
Ethical Adversaries: Towards Mitigating Unfairness with Adversarial Machine Learning. SIGKDD Explor. 23(1): 32-41 (2021) - [j14]Benoît Vanderose, Julie Henry, Benoît Frénay, Xavier Devroey:
Report from the 2nd Int. Workshop on Education through Advanced Software Engineering and Artificial Intelligence (EASEAI '20). ACM SIGSOFT Softw. Eng. Notes 46(2): 28-29 (2021) - [j13]Viet Minh Vu, Adrien Bibal, Benoît Frénay:
Constraint Preserving Score for Automatic Hyperparameter Tuning of Dimensionality Reduction Methods for Visualization. IEEE Trans. Artif. Intell. 2(3): 269-282 (2021) - [c34]Géraldin Nanfack, Valentin Delchevalerie, Benoît Frénay:
Boundary-Based Fairness Constraints in Decision Trees and Random Forests. ESANN 2021 - [c33]Valentin Delchevalerie, Alexandre Mayer, Adrien Bibal, Benoît Frénay:
Accelerating $t$-SNE using Fast Fourier Transforms and the Particle-Mesh Algorithm from Physics. IJCNN 2021: 1-8 - [c32]Jérôme Fink, Benoît Frénay, Laurence Meurant, Anthony Cleve:
LSFB-CONT and LSFB-ISOL: Two New Datasets for Vision-Based Sign Language Recognition. IJCNN 2021: 1-8 - [c31]Viet Minh Vu, Adrien Bibal, Benoît Frénay:
iPMDS: Interactive Probabilistic Multidimensional Scaling. IJCNN 2021: 1-8 - [c30]Viet Minh Vu, Adrien Bibal, Benoît Frénay:
HCt-SNE: Hierarchical Constraints with t-SNE. IJCNN 2021: 1-8 - [c29]Arnaud Bougaham, Adrien Bibal, Isabelle Linden, Benoît Frénay:
GanoDIP - GAN Anomaly Detection through Intermediate Patches: a PCBA Manufacturing Case. LIDTA@ECML/PKDD 2021: 104-117 - [c28]Valentin Delchevalerie, Adrien Bibal, Benoît Frénay, Alexandre Mayer:
Achieving Rotational Invariance with Bessel-Convolutional Neural Networks. NeurIPS 2021: 28772-28783 - [c27]Abiola Paterne Chokki, Anthony Simonofski, Benoît Frénay, Benoît Vanderose:
Open Government Data for Non-expert Citizens: Understanding Content and Visualizations' Expectations. RCIS 2021: 602-608 - [c26]Quentin Vaneck, Thomas Colart, Benoît Frénay, Benoît Vanderose:
A tool for evaluating computer programs from students. EASEAI@ESEC/SIGSOFT FSE 2021: 23-26 - [c25]Géraldin Nanfack, Paul Temple, Benoît Frénay:
Global explanations with decision rules: a co-learning approach. UAI 2021: 589-599 - [e3]Michael Kamp, Irena Koprinska, Adrien Bibal, Tassadit Bouadi, Benoît Frénay, Luis Galárraga, José Oramas, Linara Adilova, Yamuna Krishnamurthy, Bo Kang, Christine Largeron, Jefrey Lijffijt, Tiphaine Viard, Pascal Welke, Massimiliano Ruocco, Erlend Aune, Claudio Gallicchio, Gregor Schiele, Franz Pernkopf, Michaela Blott, Holger Fröning, Günther Schindler, Riccardo Guidotti, Anna Monreale, Salvatore Rinzivillo, Przemyslaw Biecek, Eirini Ntoutsi, Mykola Pechenizkiy, Bodo Rosenhahn, Christopher L. Buckley, Daniela Cialfi, Pablo Lanillos, Maxwell Ramstead, Tim Verbelen, Pedro M. Ferreira, Giuseppina Andresini, Donato Malerba, Ibéria Medeiros, Philippe Fournier-Viger, M. Saqib Nawaz, Sebastián Ventura, Meng Sun, Min Zhou, Valerio Bitetta, Ilaria Bordino, Andrea Ferretti, Francesco Gullo, Giovanni Ponti, Lorenzo Severini, Rita P. Ribeiro, João Gama, Ricard Gavaldà, Lee Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Damian Roqueiro, Diego Saldana Miranda, Konstantinos Sechidis, Guilherme Graça:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part I. Communications in Computer and Information Science 1524, Springer 2021, ISBN 978-3-030-93735-5 [contents] - [e2]Michael Kamp, Irena Koprinska, Adrien Bibal, Tassadit Bouadi, Benoît Frénay, Luis Galárraga, José Oramas, Linara Adilova, Yamuna Krishnamurthy, Bo Kang, Christine Largeron, Jefrey Lijffijt, Tiphaine Viard, Pascal Welke, Massimiliano Ruocco, Erlend Aune, Claudio Gallicchio, Gregor Schiele, Franz Pernkopf, Michaela Blott, Holger Fröning, Günther Schindler, Riccardo Guidotti, Anna Monreale, Salvatore Rinzivillo, Przemyslaw Biecek, Eirini Ntoutsi, Mykola Pechenizkiy, Bodo Rosenhahn, Christopher L. Buckley, Daniela Cialfi, Pablo Lanillos, Maxwell Ramstead, Tim Verbelen, Pedro M. Ferreira, Giuseppina Andresini, Donato Malerba, Ibéria Medeiros, Philippe Fournier-Viger, M. Saqib Nawaz, Sebastián Ventura, Meng Sun, Min Zhou, Valerio Bitetta, Ilaria Bordino, Andrea Ferretti, Francesco Gullo, Giovanni Ponti, Lorenzo Severini, Rita P. Ribeiro, João Gama, Ricard Gavaldà, Lee Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Damian Roqueiro, Diego Saldana Miranda, Konstantinos Sechidis, Guilherme Graça:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part II. Communications in Computer and Information Science 1525, Springer 2021, ISBN 978-3-030-93732-4 [contents] - [i5]Cristina Morariu, Adrien Bibal, René Cutura, Benoît Frénay, Michael Sedlmair:
DumbleDR: Predicting User Preferences of Dimensionality Reduction Projection Quality. CoRR abs/2105.09275 (2021) - 2020
- [j12]Benoît Vanderose, Benoît Frénay, Julie Henry, Xavier Devroey:
Report from the 1st Int. Workshop on Education through Advanced Software Engineering and Artificial Intelligence (EASEAI '19). ACM SIGSOFT Softw. Eng. Notes 45(1): 25-27 (2020) - [c24]Adrien Bibal, Tassadit Bouadi, Benoît Frénay, Luis Galárraga, José Oramas:
AIMLAI'20: Third Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence. CIKM 2020: 3529-3530 - [c23]Adrien Bibal, Viet Minh Vu, Géraldin Nanfack, Benoît Frénay:
Explaining t-SNE Embeddings Locally by Adapting LIME. ESANN 2020: 393-398 - [i4]Pieter Delobelle, Paul Temple, Gilles Perrouin, Benoît Frénay, Patrick Heymans, Bettina Berendt:
Ethical Adversaries: Towards Mitigating Unfairness with Adversarial Machine Learning. CoRR abs/2005.06852 (2020) - [i3]Adrien Bibal, Michael Lognoul, Alexandre de Streel, Benoît Frénay:
Impact of Legal Requirements on Explainability in Machine Learning. CoRR abs/2007.05479 (2020) - [i2]Martin Weyssow, Houari A. Sahraoui, Benoît Vanderose, Benoît Frénay:
Function completion in the time of massive data: A code embedding perspective. CoRR abs/2008.03731 (2020)
2010 – 2019
- 2019
- [j11]Rebecca Marion, Adrien Bibal, Benoît Frénay:
BIR: A method for selecting the best interpretable multidimensional scaling rotation using external variables. Neurocomputing 342: 83-96 (2019) - [c22]Jérôme Fink, Anthony Cleve, Benoît Frénay:
Deep Learning Applied to Sign Language. BNAIC/BENELEARN 2019 - [c21]Viet Minh Vu, Benoît Frénay:
User-steering interpretable visualization with probabilistic principal components analysis. ESANN 2019 - [c20]Alexandra Degeest, Michel Verleysen, Benoît Frénay:
Comparison Between Filter Criteria for Feature Selection in Regression. ICANN (2) 2019: 59-71 - [c19]Alexandra Degeest, Michel Verleysen, Benoît Frénay:
About Filter Criteria for Feature Selection in Regression. IWANN (2) 2019: 579-590 - [e1]Benoît Vanderose, Benoît Frénay, Julie Henry, Xavier Devroey:
Proceedings of the 1st ACM SIGSOFT International Workshop on Education through Advanced Software Engineering and Artificial Intelligence, EASEAI@ESEC/SIGSOFT FSE 2019, Tallinn, Estonia, August 26, 2019. ACM 2019, ISBN 978-1-4503-6852-0 [contents] - 2018
- [c18]Adrien Bibal, Rebecca Marion, Benoît Frénay:
Finding the most interpretable MDS rotation for sparse linear models based on external features. ESANN 2018 - [c17]Lauriane Castin, Benoît Frénay:
clustering with decision trees: divisive and agglomerative approach. ESANN 2018 - [c16]Benoît Frénay, Bruno Dumas, John A. Lee:
Information visualisation and machine learning: latest trends towards convergence. ESANN 2018 - [c15]Alexandra Degeest, Michel Verleysen, Benoît Frénay:
Smoothness Bias in Relevance Estimators for Feature Selection in Regression. AIAI 2018: 285-294 - 2017
- [c14]Benoît Frénay, Barbara Hammer:
Label-noise-tolerant classification for streaming data. IJCNN 2017: 1748-1755 - 2016
- [j10]Benoît Frénay, Michel Verleysen:
Reinforced Extreme Learning Machines for Fast Robust Regression in the Presence of Outliers. IEEE Trans. Cybern. 46(12): 3351-3363 (2016) - [c13]Adrien Bibal, Benoît Frénay:
Interpretability of machine learning models and representations: an introduction. ESANN 2016 - [c12]Benoît Frénay, Bruno Dumas:
Information visualisation and machine learning: characteristics, convergence and perspective. ESANN 2016 - [i1]Adrien Bibal, Benoît Frénay:
Learning Interpretability for Visualizations using Adapted Cox Models through a User Experiment. CoRR abs/1611.06175 (2016) - 2015
- [j9]Benoît Frénay, Ata Kabán:
Special issue on advances in learning with label noise. Neurocomputing 160: 1-2 (2015) - [c11]Samuel Branders, Benoît Frénay, Pierre Dupont:
Survival Analysis with Cox Regression and Random Non-linear Projections. ESANN 2015 - [c10]Alexandra Degeest, Michel Verleysen, Benoît Frénay:
Feature ranking in changing environments where new features are introduced. IJCNN 2015: 1-8 - 2014
- [j8]Benoît Frénay, Gauthier Doquire, Michel Verleysen:
Estimating mutual information for feature selection in the presence of label noise. Comput. Stat. Data Anal. 71: 832-848 (2014) - [j7]Benoît Frénay, Michel Verleysen:
Pointwise probability reinforcements for robust statistical inference. Neural Networks 50: 124-141 (2014) - [j6]Benoît Frénay, Michel Verleysen:
Classification in the Presence of Label Noise: A Survey. IEEE Trans. Neural Networks Learn. Syst. 25(5): 845-869 (2014) - [c9]Benoît Frénay, Daniela Hofmann, Alexander Schulz, Michael Biehl, Barbara Hammer:
Valid interpretation of feature relevance for linear data mappings. CIDM 2014: 149-156 - [c8]Benoît Frénay, Ata Kabán:
A comprehensive introduction to label noise. ESANN 2014 - [c7]Alexandra Degeest, Benoît Frénay, Michel Verleysen:
Automatic correction of SVM for drifted data classification. EGC 2014: 311-316 - 2013
- [b1]Benoît Frénay:
Uncertainty and label noise in machine learning. Catholic University of Louvain, Louvain-la-Neuve, Belgium, 2013 - [j5]Benoît Frénay, Mark van Heeswijk, Yoan Miche, Michel Verleysen, Amaury Lendasse:
Feature selection for nonlinear models with extreme learning machines. Neurocomputing 102: 111-124 (2013) - [j4]Benoît Frénay, Gauthier Doquire, Michel Verleysen:
Theoretical and empirical study on the potential inadequacy of mutual information for feature selection in classification. Neurocomputing 112: 64-78 (2013) - [j3]Benoît Frénay, Gauthier Doquire, Michel Verleysen:
Is mutual information adequate for feature selection in regression? Neural Networks 48: 1-7 (2013) - [c6]Gauthier Doquire, Benoît Frénay, Michel Verleysen:
Risk Estimation and Feature Selection. ESANN 2013 - 2012
- [c5]Benoît Frénay, Gauthier Doquire, Michel Verleysen:
On the Potential Inadequacy of Mutual Information for Feature Selection. ESANN 2012 - 2011
- [j2]Benoît Frénay, Michel Verleysen:
Parameter-insensitive kernel in extreme learning for non-linear support vector regression. Neurocomputing 74(16): 2526-2531 (2011) - [c4]Benoît Frénay, Gaël de Lannoy, Michel Verleysen:
Label Noise-Tolerant Hidden Markov Models for Segmentation: Application to ECGs. ECML/PKDD (1) 2011: 455-470 - 2010
- [c3]Benoît Frénay, Michel Verleysen:
Using SVMs with randomised feature spaces: an extreme learning approach. ESANN 2010
2000 – 2009
- 2009
- [j1]Benoît Frénay, Marco Saerens:
2S2, a simple reinforcement learning scheme for two-player zero-sum Markov games. Neurocomputing 72(7-9): 1494-1507 (2009) - [c2]Benoît Frénay, Gaël de Lannoy, Michel Verleysen:
Improving the transition modelling in hidden Markov models for ECG segmentation. ESANN 2009 - 2008
- [c1]Benoît Frénay, Marco Saerens:
QL2, a simple reinforcement learning scheme for two-player zero-sum Markov games. ESANN 2008: 137-142
Coauthor Index
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last updated on 2026-06-15 01:19 CEST by the dblp team
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