Benoît Frénay

发表

Classification in the Presence of Label Noise: A Survey

M. Verleysen, Benoît Frénay, 2014, IEEE Transactions on Neural Networks and Learning Systems.

Report from the 2nd Int. Workshop on Education through Advanced Software Engineering and Artificial Intelligence (EASEAI '20)

Benoît Frénay, B. Vanderose, Xavier Devroey, 2021, ACM SIGSOFT Softw. Eng. Notes.

Report from the 1st Int. Workshop on Education through Advanced Software Engineering and Artificial Intelligence (EASEAI '19)

Benoît Frénay, B. Vanderose, Xavier Devroey, 2021 .

Balancing fraud analytics with legal requirements: Governance practices and trade-offs in public administrations

M. Janssen, Benoît Frénay, Anthony Simonofski, 2022, Data & Policy.

Boundary-Based Fairness Constraints in Decision Trees and Random Forests

Benoît Frénay, Geraldin Nanfack, Valentin Delchevalerie, 2021, ESANN.

Open government data awareness: eliciting citizens’ requirements for application design

Benoît Frénay, Anthony Simonofski, B. Vanderose, 2022, Transforming Government: People, Process and Policy.

Engaging Citizens with Open Government Data: The Value of Dashboards compared to Individual Visualizations

Benoît Frénay, Anthony Simonofski, B. Vanderose, 2022, Digital Government: Research and Practice.

Fostering Interaction Between Open Government Data Stakeholders: An Exchange Platform for Citizens, Developers and Publishers

Benoît Frénay, Anthony Simonofski, B. Vanderose, 2022, EGOV.

Engaging Citizens with Open Government Data: The Value of Dashboards Compared to Individual Visualizations

Benoît Frénay, Anthony Simonofski, B. Vanderose, 2022, Digit. Gov. Res. Pract..

Constraint Enforcement on Decision Trees: A Survey

Benoît Frénay, G. Nanfack, Paul Temple, 2022, ACM Comput. Surv..

HCt-SNE: Hierarchical Constraints with t-SNE

Viet Minh Vu, Benoît Frénay, A. Bibal, 2021, 2021 International Joint Conference on Neural Networks (IJCNN).

Integrating Constraints Into Dimensionality Reduction for Visualization: A Survey

Viet Minh Vu, Benoît Frénay, A. Bibal, 2022, IEEE Transactions on Artificial Intelligence.

Predicting User Preferences of Dimensionality Reduction Embedding Quality

M. Sedlmair, Benoît Frénay, René Cutura, 2022, IEEE Transactions on Visualization and Computer Graphics.

QL2, a simple reinforcement learning scheme for two-player zero-sum Markov games

Benoît Frénay, Marco Saerens, Marco Saerens, 2009, ESANN.

A comprehensive introduction to label noise

Benoît Frénay, Ata Kabán, A. Kabán, 2014, ESANN.

Pointwise probability reinforcements for robust statistical inference

Benoît Frénay, Michel Verleysen, M. Verleysen, 2014, Neural Networks.

Label Noise-Tolerant Hidden Markov Models for Segmentation: Application to ECGs

Benoît Frénay, Michel Verleysen, Gael de Lannoy, 2011, ECML/PKDD.

Legal requirements on explainability in machine learning

Benoît Frénay, Adrien Bibal, Michael Lognoul, 2020, Artificial Intelligence and Law.

Achieving Rotational Invariance with Bessel-Convolutional Neural Networks

Benoît Frénay, Valentin Delchevalerie, Adrien Bibal, 2021, NeurIPS.

Computational Statistics and Data Analysis

Benoît Frénay, Michel Verleysen, Gauthier Doquire, 2022 .

Theoretical and empirical study on the potential inadequacy of mutual information for feature selection in classification

Benoît Frénay, Michel Verleysen, Gauthier Doquire, 2013, Neurocomputing.

Clustering Patterns of Urban Built-up Areas with Curves of Fractal Scaling Behaviour

Michel Verleysen, Pierre Frankhauser, Isabelle Thomas, 2010 .

Label-noise-tolerant classification for streaming data

Benoît Frénay, Barbara Hammer, B. Hammer, 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

Emission Modelling for Supervised ECG Segmentation using Finite Differences

Benoît Frénay, Michel Verleysen, M. Verleysen, 2008 .

Parameter-insensitive kernel in extreme learning for non-linear support vector regression

Benoît Frénay, Michel Verleysen, M. Verleysen, 2011, Neurocomputing.

Using SVMs with randomised feature spaces: an extreme learning approach

Benoît Frénay, Michel Verleysen, M. Verleysen, 2010, ESANN.

An Experimental Investigation into the Evaluation of Explainability Methods

Benoît Frénay, Nicolas Riche, Nassim Versbraegen, 2023, 2305.16361.

Learning Customised Decision Trees for Domain-knowledge Constraints

Benoît Frénay, G. Nanfack, Paul Temple, 2023, Pattern Recognition.

Feature selection for nonlinear models with extreme learning machines

Benoît Frénay, Michel Verleysen, Amaury Lendasse, 2013, Neurocomputing.

Decision trees: from efficient prediction to responsible AI

H. Blockeel, Siegfried Nijssen, Benoît Frénay, 2023, Frontiers Artif. Intell..

Reinforced Extreme Learning Machines for Fast Robust Regression in the Presence of Outliers

Benoît Frénay, Michel Verleysen, M. Verleysen, 2016, IEEE Transactions on Cybernetics.

Valid interpretation of feature relevance for linear data mappings

Benoît Frénay, Michael Biehl, Barbara Hammer, 2014, 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

Reading grid for feature selection relevance criteria in regression

Benoît Frénay, Michel Verleysen, Alexandra Degeest, 2021, Pattern Recognit. Lett..

Constrained Tiny Machine Learning for Predicting Gas Concentration with I4.0 Low-cost Sensors

Benoît Frénay, M. Adoui, Thomas Herpoel, 2023, ACM Transactions on Embedded Computing Systems.

Learning from Code Repositories to Recommend Model Classes

H. Sahraoui, Benoît Frénay, B. Vanderose, 2022, J. Object Technol..

Is mutual information adequate for feature selection in regression?

Benoît Frénay, Michel Verleysen, Gauthier Doquire, 2013, Neural Networks.

Improving the transition modelling in hidden Markov models for ECG segmentation

Benoît Frénay, Michel Verleysen, Gael de Lannoy, 2009, ESANN.

Clustering with Decision Trees: Divisive and Agglomerative Approach

Benoît Frénay, Lauriane Castin, Benoît Frénay, 2018, ESANN.

Interpretability of machine learning models and representations: an introduction

Benoît Frénay, Adrien Bibal, Benoît Frénay, 2016, ESANN.

Computational Statistics and Data Analysis Estimating Mutual Information for Feature Selection in the Presence of Label Noise

M. Verleysen, Benoît Frénay, Gauthier Doquire, 2022 .

Sign Language-to-Text Dictionary with Lightweight Transformer Models

Bruno Dumas, Benoît Frénay, Anthony Cleve, 2023, IJCAI.

LSFB-CONT and LSFB-ISOL: Two New Datasets for Vision-Based Sign Language Recognition

Benoît Frénay, Anthony Cleve, Laurence Meurant, 2021, 2021 International Joint Conference on Neural Networks (IJCNN).

Estimating mutual information for feature selection in the presence of label noise

M. Verleysen, Benoît Frénay, Gauthier Doquire, 2014, Comput. Stat. Data Anal..

Ensembles of Local Linear Models for Bankruptcy Analysis and Prediction

Benoît Frénay, Amaury Lendasse, Yoan Miche, 2011 .

BIR: A method for selecting the best interpretable multidimensional scaling rotation using external variables

Benoît Frénay, Adrien Bibal, Rebecca Marion, 2019, Neurocomputing.

User-steering interpretable visualization with probabilistic principal components analysis

Benoît Frénay, Viet Minh Vu, Benoît Frénay, 2019, ESANN.

Survival Analysis with Cox Regression and Random Non-linear Projections

Benoît Frénay, Pierre Dupont, Samuel Branders, 2015, ESANN.

About Filter Criteria for Feature Selection in Regression

Benoît Frénay, Michel Verleysen, Alexandra Degeest, 2019, IWANN.

Smoothness Bias in Relevance Estimators for Feature Selection in Regression

Benoît Frénay, Michel Verleysen, Alexandra Degeest, 2018, AIAI.

Feature ranking in changing environments where new features are introduced

Benoît Frénay, Michel Verleysen, Alexandra Degeest, 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

Finding the most interpretable MDS rotation for sparse linear models based on external features

Benoît Frénay, Adrien Bibal, Rebecca Marion, 2018, ESANN.

where New Features are Introduced

Benoît Frénay, Alexandra Degeest, Haute-Ecole Paul-Henri Spaak, 2015 .

Global explanations with decision rules: a co-learning approach

Benoît Frénay, Paul Temple, Géraldin Nanfack, 2021, UAI.

DT-SNE: t-SNE discrete visualizations as decision tree structures

Benoît Frénay, Valentin Delchevalerie, Adrien Bibal, 2023, Neurocomputing.

A tool for evaluating computer programs from students

Benoît Frénay, Benoît Vanderose, Quentin Vaneck, 2021, EASEAI@ESEC/SIGSOFT FSE.

A comprehensive introduction to label noise

Benoît Frénay, Ata Kabán, A. Kabán, 2014, The European Symposium on Artificial Neural Networks.

Uncertainty and label noise in machine learning

Benoît Frénay, Benoît Frénay, 2013 .