Source Themes | Geraldin Nanfack

Source Themes

An Experimental Investigation into the Evaluation of Explainability Methods
EXplainable Artificial Intelligence (XAI) aims at helping users to grasp the reasoning behind the predictions of an Artificial …
Śedrick Stassin, Alexandre Englebert, Gilles Peiffer, Géraldin Nanfack, Julien Albert, Nassim Versbraegen, Miriam Doh, Nicolas Riche, Benoît Frénay, Christophe De Vleeschouwer
Project
Decision trees: From efficient prediction to responsible AI
should be released soon…
Hendrik Blockeel, Laurens Devos, Benoît Frénay, Géraldin Nanfack, Siegfried Nijssen
Project
Constraint Enforcement on Decision Trees: a Survey
Decision trees have the particularity of being machine learning models that are visually easy to interpret and understand. Therefore, …
Géraldin Nanfack, Paul Temple, Benoît Frénay
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Constraint Enforcement on Decision Trees: a Survey
Learning Customised Decision Trees for Domain-knowledge Constraints
When applied to critical domains, machine learning models usually need to comply with prior knowledge and domain-specific requirements. …
Géraldin Nanfack, Paul Temple, Benoît Frénay
Project
Learning Customised Decision Trees for Domain-knowledge Constraints
Boundary-Based Fairness Constraints in Decision Trees and Random Forests
Decision Trees (DTs) and Random Forests (RFs) are popular models in Machine Learning (ML) thanks to their interpretability and …
Géraldin Nanfack, Valentin Delchevalerie, Benoît Frénay
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Boundary-Based Fairness Constraints in Decision Trees and Random Forests
Global Explanations with Decision Rules: a Co-learning Approach
Black-box machine learning models can be extremely accurate. Yet, in critical applications such as in healthcare or justice, if models …
Géraldin Nanfack, Paul Temple, Benoît Frénay
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Global Explanations with Decision Rules: a Co-learning Approach
Explaining t-SNE Embeddings Locally by Adapting LIME
Decision Trees (DTs) and Random Forests (RFs) are popular models in Machine Learning (ML) thanks to their interpretability and …
Adrien Bibal, Minh Viet Vu, Géraldin Nanfack, Benoît Frénay
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Explaining t-SNE Embeddings Locally by Adapting LIME
Squeeze-SegNet: a new fast deep convolutional neural network for semantic segmentation
The recent researches in Deep Convolutional Neural Network have focused their attention on improving accuracy that provide significant …
Géraldin Nanfack, Azeddine Elhassouny, Rachid Oulad Haj Thami
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Squeeze-SegNet: a new fast deep convolutional neural network for semantic segmentation
An example journal article
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Géraldin Nanfack, Robert Ford
Sep 1, 2015
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An example journal article