Alex Aussem

Alex Aussem, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

A Conservative Feature Subset Selection Algorithm with Missing Data

Alex Aussem, Sergio Rodrigues de Morais, A. Aussem, 2008, 2008 Eighth IEEE International Conference on Data Mining.

Exploiting Data Missingness in Bayesian Network Modeling

Alex Aussem, Sergio Rodrigues de Morais, A. Aussem, 2009, IDA.

Reducing variance due to importance weighting in covariate shift bias correction

Van-Tinh Tran, Alex Aussem, A. Aussem, 2017, ESANN.

On the use of binary stochastic autoencoders for multi-label classification under the zero-one loss

Alex Aussem, Denis Lecoeuche, Maxime Gasse, 2018, INNS Conference on Big Data.

Queueing network modelling with distributed neural networks for service quality estimation in B-ISDN networks

Alex Aussem, Antoine Mahul, Raymond Marie, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

Hierarchical Recurrent Attention Networks for Context-Aware Education Chatbots

Alex Aussem, Jean-Baptiste Aujogue, 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

A semi-supervised feature ranking method with ensemble learning

Haytham Elghazel, Alex Aussem, Fazia Bellal, 2012, Pattern Recognit. Lett..

Learning the local Bayesian network structure around the ZNF217 oncogene in breast tumours

Alex Aussem, Sergio Rodrigues de Morais, Christian Gautier, 2013, Comput. Biol. Medicine.

e-functional dependency inference: application to DNA microarray expression data

Jean-Marc Petit, Alex Aussem, A. Aussem, 2001, BDA.

Neural network modelling for environmental prediction

Fionn Murtagh, Alex Aussem, G. Zheng, 2000, Neurocomputing.

Neural-network metamodelling for the prediction of Caulerpa taxifolia development in the Mediterranean sea

David R. C. Hill, Alex Aussem, A. Aussem, 2000, Neurocomputing.

Ensemble Multi-label Classification: A Comparative Study on Threshold Selection and Voting Methods

Haytham Elghazel, Alex Aussem, Ouadie Gharroudi, 2015, 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI).

A Semi-Supervised Ensemble Approach for Multi-label Learning

Haytham Elghazel, Alex Aussem, Ouadie Gharroudi, 2016, 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW).

Incremental Bayesian Network Learning for Scalable Feature Selection

Stéphane Bonnevay, Alex Aussem, Grégory Thibault, 2009, IDA.

An Empirical Comparison of Supervised Ensemble Learning Approaches

Haytham Elghazel, Alex Aussem, Mohamed Bibimoune, 2013 .

Apprentissage de la structure des réseaux bayésiens à partir des motifs fréquents corrélés : application à l'identification des facteurs environnementaux du cancer du Nasopharynx

Fabien De Marchi, Alex Aussem, Marilys Corbex, 2006, EGC.

A Practical Approach to Reduce the Learning Bias Under Covariate Shift

Van-Tinh Tran, Alex Aussem, A. Aussem, 2015, ECML/PKDD.

A Novel Scalable and Data Efficient Feature Subset Selection Algorithm

Alex Aussem, Sergio Rodrigues de Morais, A. Aussem, 2008, ECML/PKDD.

Learning Bayesian network structures by estimation of distribution algorithms: An experimental analysis

Stéphane Bonnevay, Alex Aussem, Grégory Thibault, 2007, ICDIM.

Dynamic Ensemble Selection with Probabilistic Classifier Chains

Haytham Elghazel, Alex Aussem, Anil Narassiguin, 2017, ECML/PKDD.

Analysis of nasopharyngeal carcinoma risk factors with Bayesian networks

Alex Aussem, Sergio Rodrigues de Morais, Marilys Corbex, 2012, Artif. Intell. Medicine.

Unsupervised feature selection with ensemble learning

Haytham Elghazel, Alex Aussem, A. Aussem, 2013, Machine Learning.

Feature Selection for Unsupervised Learning Using Random Cluster Ensembles

Haytham Elghazel, Alex Aussem, 2010, 2010 IEEE International Conference on Data Mining.

Dynamical recurrent neural networks towards prediction and modeling of dynamical systems

Alex Aussem, A. Aussem, 1999, Neurocomputing.

End-to-End Extraction of Structured Information from Business Documents with Pointer-Generator Networks

Véronique Eglin, Haytham Elghazel, Alex Aussem, 2020, SPNLP.

Le Calcul du Gradient d'Erreur dans les Réseaux de Neurones : Applications aux Telecom et aux Sciences Environnementales

Alex Aussem, 2002 .

A novel Markov boundary based feature subset selection algorithm

Alex Aussem, Sergio Rodrigues de Morais, A. Aussem, 2010, Neurocomputing.

Web traffic demand forecasting using wavelet-based multiscale decomposition

Fionn Murtagh, Alex Aussem, 2001, Int. J. Intell. Syst..

A novel scalable and correct Markov boundary learning algorithms under faithfulness condition

Alex Aussem, Sergio Rodrigues de Morais, A. Aussem, 2008 .

Trading-Off Diversity and Accuracy for Optimal Ensemble Tree Selection in Random Forests

Haytham Elghazel, Alex Aussem, Florence Perraud, 2011, Ensembles in Machine Learning Applications.

Recurrent Neural Network Approach for Table Field Extraction in Business Documents

Véronique Eglin, Haytham Elghazel, Alex Aussem, 2019, 2019 International Conference on Document Analysis and Recognition (ICDAR).

Semi-supervised Feature Importance Evaluation with Ensemble Learning

Haytham Elghazel, Alex Aussem, Hasna Barkia, 2011, 2011 IEEE 11th International Conference on Data Mining.

Dynamical recurrent neural networks -- towards environmental time series prediction

Fionn Murtagh, Alex Aussem, Marc Sarazin, 1995, Int. J. Neural Syst..

Correcting a Class of Complete Selection Bias with External Data Based on Importance Weight Estimation

Van-Tinh Tran, Alex Aussem, A. Aussem, 2015, ICONIP.

Intégration de contraintes dans les cartes auto-organisatrices

Khalid Benabdeslem, Bruno Canitia, Alex Aussem, 2008, EGC.

Approche connexionniste pour l'extraction de profils cas-témoins du cancer du Nasopharynx à partir des données issues d'une étude épidémiologique

Mustapha Lebbah, Khalid Benabdeslem, Alex Aussem, 2007, EGC.

Segmentation of switching dynamics with a Hidden Markov Model of neural prediction experts

Alex Aussem, C. Boutevin, A. Aussem, 2001, ESANN.

Modelling switching dynamics using prediction experts operating on distinct wavelet scales

Pierre Chainais, Alex Aussem, 2006, ESANN.

New Problems and Approaches Related to Large Databases in Astronomy

Fionn Murtagh, Alex Aussem, F. Murtagh, 1997 .

Combining Neural Network Forecasts on Wavelet-transformed Time Series

Fionn Murtagh, Alex Aussem, F. Murtagh, 1997, Connect. Sci..

A novel Bayesian Network structure learning algorithm based on minimal correlated itemset mining techniques

Alex Aussem, Zahra Kebaili, A. Aussem, 2007, 2007 2nd International Conference on Digital Information Management.

An Efficient and Scalable Algorithm for Local Bayesian Network Structure Discovery

Alex Aussem, Sergio Rodrigues de Morais, A. Aussem, 2010, ECML/PKDD.

A conservative feature subset selection algorithm with missing data

Alex Aussem, Sergio Rodrigues de Morais, 2010, Neurocomputing.

F-Measure Maximization in Multi-Label Classification with Conditionally Independent Label Subsets pdf

Alex Aussem, Maxime Gasse, 2016, ECML/PKDD.

Identifying the irreducible disjoint factors of a multivariate probability distribution

Alex Aussem, Maxime Gasse, 2016, Probabilistic Graphical Models.

Calibrated k-labelsets for Ensemble Multi-label Classification

Haytham Elghazel, Alex Aussem, Ouadie Gharroudi, 2015, ICONIP.

Modeling the Ultimate Seaweed Expansion

David R. C. Hill, Alex Aussem, Patrick Coquillard, 2001, Simul..

Bayesian networks

Alex Aussem, 2010, Neurocomputing.

Nasopharyngeal Carcinoma Data Analysis with a Novel Bayesian Network Skeleton Learning Algorithm

Alex Aussem, Sergio Rodrigues de Morais, Marilys Corbex, 2007, AIME.

A Comparison of Multi-Label Feature Selection Methods Using the Random Forest Paradigm

Haytham Elghazel, Alex Aussem, Ouadie Gharroudi, 2014, Canadian Conference on AI.

Ensemble multi-label text categorization based on rotation forest and latent semantic indexing

Haytham Elghazel, Alex Aussem, Ouadie Gharroudi, 2016, Expert Syst. Appl..

Handling almost-deterministic relationships in constraint-based Bayesian network discovery : Application to cancer risk factor identification

Alex Aussem, Sergio Rodrigues de Morais, Marilys Corbex, 2008, ESANN.

Neural Embedded Dirichlet Processes for Topic Modeling

Stéphane Bonnevay, Alex Aussem, Bruno Canitia, 2021, MDAI.

Data-Efficient Information Extraction from Documents with Pre-trained Language Models

Christophe Garcia, Stefan Duffner, Véronique Eglin, 2021, ICDAR Workshops.