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2020

De-constructing Media-fueled Conceptions on Artificial Intelligence by Playing “Who’s who?”

Collard, A-S., Henry, J., Hernalesteen, A., Jacques, J. & Frenay, B., 2020, Proceedings of Media Education Summitt 2020.

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2019

About Filter Criteria for Feature Selection in Regression

Degeest, A., Verleysen, M. & Frénay, B., 1 Jan 2019, Advances in Computational Intelligence - 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Proceedings. Rojas, I., Joya, G. & Catala, A. (eds.). Springer Verlag, p. 579-590 12 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11507 LNCS).

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

Comparison Between Filter Criteria for Feature Selection in Regression

Degeest, A., Verleysen, M. & Frénay, B., 1 Jan 2019, Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning - 28th International Conference on Artificial Neural Networks, Proceedings. Tetko, I. V., Karpov, P., Theis, F. & Kurková, V. (eds.). Springer Verlag, p. 59-71 13 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11728 LNCS).

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

Deep Learning Applied to Sign Language

Fink, J., Cleve, A. & Frenay, B., 1 Jan 2019, Reference AI & ML Conference for Belgium, Netherlands & Luxemburg 2019.: Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019.. Beuls, K., Bogaerts, B., Bontempi, G., Geurts, P., Harley, N., Lebichot, B., Lenaerts, T., Louppe, G. & Van Ecke, P. (eds.). CEUR Workshop Proceedings, Vol. 2491. (CEUR Workshop Proceedings).

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Open Access
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Measuring Quality and Interpretability of Dimensionality Reduction Visualizations

Bibal, A. & Frenay, B., 2019, SafeML ICLR Workshop. New Orleans, Louisiana

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Open Access
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User-Based Experiment Guidelines for Measuring Interpretability in Machine Learning

Bibal, A., Dumas, B. & Frenay, B., 2019, EGC Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence. Metz

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Open Access
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User-steering Interpretable Visualization with Probabilistic Principal Components Analysis

Vu, V. M. & Frenay, B., 28 Mar 2019, ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication, p. 349-354 6 p.

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Open Access
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2018

Clustering with decision trees: Divisive and agglomerative approach

Castin, L. & Frénay, B., 1 Jan 2018, ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication, p. 455-460 6 p. (ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

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Comprendre l’ordinateur à travers un système informatique tangible, le micro:bit

Théate, N., Smal, A., Frenay, B. & Henry, J., 27 Mar 2018, Une école numérique pour émanciper ?: Colloque scientifique, Actes de la conférence. 4 p.

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Open Access
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Finding the Most Interpretable MDS Rotation for Sparse Linear Models based on External Features

Bibal, A., Marion, R. & Frenay, B., 1 Jan 2018, 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, p. 537-542 6 p. (ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

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Open Access
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Intelligence Artificielle: Éduquer pour modifier la représentation qu’en ont les jeunes

Olivier, B., Smal, A., Frenay, B. & Henry, J., 27 Mar 2018, Une école numérique pour émanciper ?: Colloque scientifique, Actes de la conférence. p. 34-37 4 p.

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Open Access
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Interaction and User Integration in Machine Learning for Information Visualisation

Dumas, B., Frenay, B. & Lee, J., 25 Apr 2018, ESANN 2018 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com.publ., p. 97-104 8 p. (ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

Smoothness bias in relevance estimators for feature selection in regression

Degeest, A., Verleysen, M. & Frénay, B., 1 Jan 2018, Artificial Intelligence Applications and Innovations - 14th IFIP WG 12.5 International Conference, AIAI 2018, Proceedings. Maglogiannis, I., Iliadis, L. & Plagianakos, V. (eds.). Springer New York, p. 285-294 10 p. (IFIP Advances in Information and Communication Technology; vol. 519).

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

2017

Label-noise-tolerant classification for streaming data

Frenay, B. & Hammer, B., 30 Jun 2017, 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., Vol. 2017-May. p. 1748-1755 8 p. 7966062

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Open Access
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4 Downloads (Pure)
2016

Information Visualisation and Machine Learning: Characteristics, Convergence and Perspective

Frenay, B. & Dumas, B., 29 Apr 2016, ESANN 2016: Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication, p. 623-628 6 p.

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Interpretability of Machine Learning Models and Representations: an Introduction

Bibal, A. & Frenay, B., 2016, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, p. 77-82

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Open Access
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626 Downloads (Pure)

Introduction to Interpretability in Machine Learning

Bibal, A. & Frenay, B., 2016, BENELEARN 2016. Kortrijk

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Open Access
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Learning Interpretability for Visualizations using Adapted Cox Models through a User Experiment

Bibal, A. & Frenay, B., 2016, NIPS Workshop on Interpretable Machine Learning in Complex Systems. Barcelona

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Open Access
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Robustifying Maximum Likelihood Inference

Frenay, B. & Verleysen, M., 2016, BENELEARN 2016. Kortrijk

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2015

Feature ranking in changing environments where new features are introduced

Degeest, A., Verleysen, M. & Frénay, B., 28 Sep 2015, Proceedings of the International Joint Conference on Neural Networks. Institute of Electrical and Electronics Engineers Inc., Vol. 2015-September. p. 1-8 8 p. 7280533

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Survival analysis with Cox regression and random non-linear projections

Branders, S., Frénay, B. & Dupont, P., 2015, 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015 - Proceedings. i6doc.com publication, p. 119-124 6 p.

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution