Haytham Elghazel

According to our database, Haytham Elghazel authored at least 54 papers between 2006 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

A new sentence embedding framework for the education and professional training domain with application to hierarchical multi-label text classification.

Guillaume Lefebvre, Haytham Elghazel, Théodore Guillet, Alexandre Aussem, Matthieu Sonnati

Data Knowl. Eng., 2024

PAC-Bayesian Domain Adaptation Bounds for Multi-view learning.

Mehdi Hennequin, Khalid Benabdeslem, Haytham Elghazel

CoRR, 2024

2023

BERTEPro : A new Sentence Embedding Framework for the Education and Professional Training domain.

Guillaume Lefebvre, Haytham Elghazel, Théodore Guillet, Alexandre Aussem, Matthieu Sonnati

Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, 2023

BERTEPro : Une nouvelle approche de représentation sémantique dans le domaine de l'éducation et de la formation professionnelle.

Guillaume Lefebvre, Haytham Elghazel, Théodore Guillet, Alexandre Aussem, Matthieu Sonnati

Proceedings of the Extraction et Gestion des Connaissances, 2023

Classification multi-label de données médicales par LSTM temporel et clustering flou.

Abdelhamid Gaddari, Haytham Elghazel, Rakia Jaziri, Mohand-Saïd Hacid, Pierre-Henri Comble

Proceedings of the Extraction et Gestion des Connaissances, 2023

Accounting for Imputation Uncertainty During Neural Network Training.

Thomas Ranvier, Haytham Elghazel, Emmanuel Coquery, Khalid Benabdeslem

Proceedings of the Big Data Analytics and Knowledge Discovery, 2023

A New Time-Aware LSTM based Framework for Multi-label Classification on Healthcare Data.

Abdelhamid Gaddari, Haytham Elghazel, Rakia Jaziri, Mohand-Said Hacid, Pierre-Henri Comble

Proceedings of the 20th ACS/IEEE International Conference on Computer Systems and Applications, 2023

2022

Adversarial Multi - View Domain Adaptation for Regression.

Mehdi Hennequin, Khalid Benabdeslem, Haytham Elghazel

Proceedings of the International Joint Conference on Neural Networks, 2022

Localized Feature Ranking approach for Multi-Target Regression.

Hugo Bertrand, Haytham Elghazel, Sahar Masmoudi, Emmanuel Coquery, Mohand-Saïd Hacid

Proceedings of the International Joint Conference on Neural Networks, 2022

Autoencoder-Based Attribute Noise Handling Method for Medical Data.

Thomas Ranvier, Haytham Elghazel, Emmanuel Coquery, Khalid Benabdeslem

Proceedings of the Neural Information Processing - 29th International Conference, 2022

Multi-view Self-attention for Regression Domain Adaptation with Feature Selection.

Mehdi Hennequin, Khalid Benabdeslem, Haytham Elghazel, Thomas Ranvier, Eric Michoux

Proceedings of the Neural Information Processing - 29th International Conference, 2022

2021

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

Clément Sage, Thibault Douzon, Alex Aussem, Véronique Eglin, Haytham Elghazel, Stefan Duffner, Christophe Garcia, Jérémy Espinas

Proceedings of the Document Analysis and Recognition, 2021

2020

Social Media Data Integration: From Data Lake to NoSQL Data Warehouse.

Hichem Dabbèchi, Nahla Zaaboub Haddar, Haytham Elghazel, Kais Haddar

Proceedings of the Intelligent Systems Design and Applications, 2020

Identifying Conditionally Independent Target Subsets for Multi-Target Regression*Note: Sub-titles are not captured in Xplore and should not be used.

Orhan Yazar, Haytham Elghazel, Mohand-Saïd Hacid, Nathalie Castin

Proceedings of the 32nd IEEE International Conference on Tools with Artificial Intelligence, 2020

NoSQL Data Lake: A Big Data Source from Social Media.

Hichem Dabbèchi, Nahla Zaaboub Haddar, Haytham Elghazel, Kais Haddar

Proceedings of the Hybrid Intelligent Systems, 2020

Streaming Time Series Forecasting using Multi-Target Regression with Dynamic Ensemble Selection.

Dihia Boulegane, Albert Bifet, Haytham Elghazel, Giyyarpuram Madhusudan

Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

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

Clément Sage, Alex Aussem, Véronique Eglin, Haytham Elghazel, Jérémy Espinas

Proceedings of the Fourth Workshop on Structured Prediction for NLP@EMNLP 2020, 2020

2019

Conditionally Decorrelated Multi-Target Regression.

Orhan Yazar, Haytham Elghazel, Mohand-Said Hacid, Nathalie Castin

Proceedings of the Neural Information Processing - 26th International Conference, 2019

Recurrent Neural Network Approach for Table Field Extraction in Business Documents.

Clément Sage, Alexandre Aussem, Haytham Elghazel, Véronique Eglin, Jérémy Espinas

Proceedings of the 2019 International Conference on Document Analysis and Recognition, 2019

2017

Dynamic Ensemble Selection with Probabilistic Classifier Chains.

Anil Narassiguin, Haytham Elghazel, Alex Aussem

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

2016

An extensive empirical comparison of ensemble learning methods for binary classification.

Anil Narassiguin, Mohamed Bibimoune, Haytham Elghazel, Alex Aussem

Pattern Anal. Appl., 2016

Ensemble constrained Laplacian score for efficient and robust semi-supervised feature selection.

Khalid Benabdeslem, Haytham Elghazel, Mohammed M. Al-Hindawi

Knowl. Inf. Syst., 2016

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

Haytham Elghazel, Alex Aussem, Ouadie Gharroudi, Wafa Saadaoui

Expert Syst. Appl., 2016

Semi-supervised co-selection: Features and instances by a weighting approach.

Raywat Makkhongkaew, Khalid Benabdeslem, Haytham Elghazel

Proceedings of the 2016 International Joint Conference on Neural Networks, 2016

Similarity Tree Pruning: A Novel Dynamic Ensemble Selection Approach.

Anil Narassiguin, Haytham Elghazel, Alex Aussem

Proceedings of the IEEE International Conference on Data Mining Workshops, 2016

A Semi-Supervised Ensemble Approach for Multi-label Learning.

Ouadie Gharroudi, Haytham Elghazel, Alexandre Aussem

Proceedings of the IEEE International Conference on Data Mining Workshops, 2016

2015

Unsupervised feature selection with ensemble learning.

Haytham Elghazel, Alex Aussem

Mach. Learn., 2015

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

Ouadie Gharroudi, Haytham Elghazel, Alex Aussem

Proceedings of the 27th IEEE International Conference on Tools with Artificial Intelligence, 2015

Calibrated k-labelsets for Ensemble Multi-label Classification.

Ouadie Gharroudi, Haytham Elghazel, Alex Aussem

Proceedings of the Neural Information Processing - 22nd International Conference, 2015

On the Optimality of Multi-Label Classification under Subset Zero-One Loss for Distributions Satisfying the Composition Property.

Maxime Gasse, Alexandre Aussem, Haytham Elghazel

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

Different Aspects of Clustering The Self-Organizing Maps.

Haytham Elghazel, Khalid Benabdeslem

Neural Process. Lett., 2014

A hybrid algorithm for Bayesian network structure learning with application to multi-label learning.

Maxime Gasse, Alex Aussem, Haytham Elghazel

Expert Syst. Appl., 2014

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

Ouadie Gharroudi, Haytham Elghazel, Alex Aussem

Proceedings of the Advances in Artificial Intelligence, 2014

2012

A semi-supervised feature ranking method with ensemble learning.

Fazia Bellal, Haytham Elghazel, Alex Aussem

Pattern Recognit. Lett., 2012

An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning.

Maxime Gasse, Alex Aussem, Haytham Elghazel

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012

A Relational-Based Approach for Aggregated Search in Graph Databases.

Thanh-Huy Le, Haytham Elghazel, Mohand-Said Hacid

Proceedings of the Database Systems for Advanced Applications, 2012

2011

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

Haytham Elghazel, Alex Aussem, Florence Perraud

Proceedings of the Ensembles in Machine Learning Applications, 2011

Semi-supervised Feature Importance Evaluation with Ensemble Learning.

Hasna Barkia, Haytham Elghazel, Alex Aussem

Proceedings of the 11th IEEE International Conference on Data Mining, 2011

Aggregated Search in Graph Databases: Preliminary Results.

Haytham Elghazel, Mohand-Said Hacid

Proceedings of the Graph-Based Representations in Pattern Recognition, 2011

Graph Aggregation Based Image Modeling and Indexing for Video Annotation.

Najib Ben Aoun, Haytham Elghazel, Mohand-Said Hacid, Chokri Ben Amar

Proceedings of the Computer Analysis of Images and Patterns, 2011

A Graph Enrichment Based Clustering over Vertically Partitioned Data.

Khalid Benabdeslem, Brice Effantin, Haytham Elghazel

Proceedings of the Advanced Data Mining and Applications - 7th International Conference, 2011

2010

Feature Selection for Unsupervised Learning Using Random Cluster Ensembles.

Haytham Elghazel, Alex Aussem

Proceedings of the ICDM 2010, 2010

A Graph Based Framework for Clustering and Characterization of SOM.

Rakia Jaziri, Khalid Benabdeslem, Haytham Elghazel

Proceedings of the Artificial Neural Networks - ICANN 2010, 2010

Consensus clustering by graph based approach.

Haytham Elghazel, Khalid Benabdeslem, Fatma Hamdi

Proceedings of the 18th European Symposium on Artificial Neural Networks, 2010

2009

Towards B-Coloring of SOM.

Haytham Elghazel, Khalid Benabdeslem

Proceedings of the Machine Learning and Data Mining in Pattern Recognition, 2009

McSOM: Minimal Coloring of Self-Organizing Map.

Haytham Elghazel, Khalid Benabdeslem, Hamamache Kheddouci

Proceedings of the Advanced Data Mining and Applications, 5th International Conference, 2009

2008

A Graph b-coloring Framework for Data Clustering.

Haytham Elghazel, Hamamache Kheddouci, Véronique Deslandres, Alain Dussauchoy

J. Math. Model. Algorithms, 2008

An Integrated Graph and Probability Based Clustering Framework for Sequential Data.

Haytham Elghazel, Tetsuya Yoshida, Mohand-Said Hacid

Proceedings of the Discovery Science, 11th International Conference, 2008

2007

Clinical pathway analysis using graph-based approach and Markov models.

Haytham Elghazel, Véronique Deslandres, Kassem Kallel, Alain Dussauchoy

Proceedings of the Second IEEE International Conference on Digital Information Management (ICDIM), 2007

A New Greedy Algorithm for Improving b-Coloring Clustering.

Haytham Elghazel, Tetsuya Yoshida, Véronique Deslandres, Mohand-Said Hacid, Alain Dussauchoy

Proceedings of the Graph-Based Representations in Pattern Recognition, 2007

A Partially Dynamic Clustering Algorithm for Data Insertion and Removal.

Haytham Elghazel, Hamamache Kheddouci, Véronique Deslandres, Alain Dussauchoy

Proceedings of the Discovery Science, 10th International Conference, 2007

Constrained Graph b-Coloring Based Clustering Approach.

Haytham Elghazel, Khalid Benabdeslem, Alain Dussauchoy

Proceedings of the Data Warehousing and Knowledge Discovery, 9th International Conference, 2007

2006

A New Clustering Approach for Symbolic Data and Its Validation: Application to the Healthcare Data.

Haytham Elghazel, Véronique Deslandres, Mohand-Said Hacid, Alain Dussauchoy, Hamamache Kheddouci

Proceedings of the Foundations of Intelligent Systems, 16th International Symposium, 2006

A New Clustering Approach for Symbolic Data: Algorithms and Application to Healthcare Data.

Haytham Elghazel, Mohand-Said Hacid, Hamamache Kheddouci, Alain Dussauchoy

Proceedings of the 22èmes Journées Bases de Données Avancées, 2006