Haytham Elghazel

Welcome to my Personal HomePage ...

I am Haytham Elghazel, Associate Professor in Computer Science at Polytech Lyon.
I am a member of the LIRIS Laboratory (DM2L team).

Research

My research focuses on the development of principled approaches to machine learning, statistical analysis and pattern recognition and their application to diverse areas including bioinformatics, fault detection, forecasting, recommendation, process monitoring, medical diagnosis, etc. My specific aim is to use modern machine learning techniques, especially ensemble methods for solving various problems:

• Supervised learning
• Multiple output learning
• Unsupervised learning
• Semi-supervised learning
• Feature selection
• Co-selection : instances and features

Teaching

My teaching is concerned with Business Intelligence and Advanced Data Analysis. These subjects are taught at 3rd, 4th and 5th years in Polytech Lyon Engineering School and M2 level in the department of computer science of Université Claude Bernard Lyon 1 and EM Lyon.

I am also the co-leader of the Data Science Master at Université Claude Bernard Lyon 1.

My current teaching :

• Business Intelligence
• Advanced Databases
• Machine Learning
• Data Mining
• Big Data in practice
• Multi-dimensional Data Analysis

Projects and collaborations

My works are used in various international, national and industrial projects:

Publications

• Ph.D. Thesis

  H. Elghazel. Classification et Prévision des Données Hétérogènes : Application aux Trajectoires et Séjours Hospitaliers, Mémoire de thèse, Université Claude Bernard Lyon 1, Lyon, France. Décembre, 2007. [Thesis]

• International Journals

G. Lefebvre, H. Elghazel, T. Guillet, A. Aussem and M. Sonnati. A new Sentence Embedding Framework for the Education and Professional Training Domain with Application to Hierarchical Multi-Label Text Classification, Data and Knowledge Engineering, Elsevier, 2024.
S. Masmoudi, H. Elghazel, D. Taieb, O. Yazar and A. Kallel. A Machine-learning Framework for Predicting Multiple Air Pollutants Concentrations via Multi-target Regression and Feature Selection, Science of the Total Environment, Elsevier, 715:136991, (DOI:10.1016/j.scitotenv.2020.136991), 2020.
H. Elghazel, A. Aussem, O. Gharroudi and W. Saadaoui. Ensemble Multi-label Text Categorization based on Rotation Forest and Latent Semantic Indexing, Expert Systems with Applications, Elsevier, Volume 57, pages 1-11, 2016.
A. Narassiguin, M. Bibimoune, H. Elghazel and A. Aussem. An Extensive Empirical Comparison of Ensemble Learning Methods for Binary Classification, Pattern Analysis and Applications, Springer, Volume 19, Number 4, pages 1093-1128, 2016.
F. Magrangeas, R. Kuiper, H. Avet-Loiseau, W. Gourraud, C. Guérin-Charbonnel, L. Ferrer, A. Aussem, H. Elghazel, J. Suhard, H. Der Sarkissian, M. Attal, N. Munshi, P. Sonneveld, C. Dumontet, P. Moreau, M. Van Duin, L. Campion and S. Minvielle. A Genome-Wide Association Study Identifies a Novel Locus for Bortezomib-Induced Peripheral Neuropathy in European Multiple Myeloma Patients, Clinical Cancer Research, 2016. (to appear)
K. Benabdeslem, H. Elghazel and M. Hindawi. Ensemble Constrained Laplacian score for efficient and robust semi-supervised feature selection, Knowledge and Information Systems, Volume 49, Number 3, pages 1161-1185, 2016.
H. Elghazel and A. Aussem. Unsupervised Feature Selection with Ensemble Learning, Machine Learning, Volume 98, Number 1-2, pages 157-180, 2015.
M. Gasse, A. Aussem and H. Elghazel. A hybrid algorithm for Bayesian network structure learning with application to multi-label learning, Expert Systems with Applications, Elsevier, Volume 41, Number 15, pages 6755-6772, 2014.
H. Elghazel and K. Benabdeslem. Different aspects for clustering self-organizing maps, Neural Processing Letters, Springer US, Volume 39, Number 1, pages 97-114, 2014.
F. Bellal, H. Elghazel and A. Aussem. A Semi Supervised Feature Ranking Method with Ensemble Learning, Pattern Recognition Letters, Elsevier, Volume 33, Number 10, pages 1426-1432, 2012.
H. Elghazel, V. Deslandres, K. Kallel and A. Dussauchoy. Clinical Pathway Clustering Using Graph b-coloring and Markov Model, International Journal of Biomedical Engineering and Technology-Special Issue on "Warehousing and Mining Complex Data: Applications to Biology, Medicine, Behavior, Health and Environment", Inderscience, Vol. 3, No. 1/2 , pages 156-172, 2010.
H. Elghazel, H. Kheddouci, V. Deslandres and A. Dussauchoy. A Graph b-coloring Framework for Data Clustering, Journal of Mathematical Modelling and Algorithms, © Springer Verlag, Volume 7, Number 4, pages 389-423, 2008.

• National Journals

K. Benabdeslem, H. Elghazel and R. Jaziri. Un cadre graphique pour la visualisation et la caractérisation de classes en mode non supervisé, Revue des nouvelles technologies de l’information, RNTI, Edition Cépaduès, 2010.

• International Book Chapters

H. Elghazel, A. Aussem and F. Perraud. Trading-Off Diversity and Accuracy for Optimal Ensemble Tree Selection in Random Forests , In: Ensembles in Machine Learning Applications, Studies in Computational Intelligence, © Springer Verlag, O. Okun, G. Valentini and M. Re (Eds), pages 169-179, 2011.
T. Yoshida, H. Elghazel, V. Deslandres, M-S. Hacid and A. Dussauchoy. Toward Improving b-Coloring based Clustering using a Greedy re-Coloring Algorithm, In: Advances in Greedy Algorithms, W. Bednorz (Eds), I-Tech Education and Publishing, ISBN: 978-953-7619-27-5, pages 553-568, 2008.
H. Elghazel, M. Azzaoui, J. Legrand and M-S. Hacid. KSyDoC: A Keyword-based System for DOcument Clustering and Retrieval, In: Bridging Archaeological and Information Technology Culture for community accessibility, L’Erma di Bretschneider, G. BAGNASCO (Eds), ISBN: 978-88-8265-488-7, pages 101-106, 2008.

• International Conferences

T. Ranvier, H. Elghazel, E. Coquery and K. Benabdeslem. Accounting for imputation uncertainlty during neural network training, In the proceedings of the International Conference on Data Warehousing and Knowledge Discovery (DAWAK 2023), Penang, Malaysia, August 28-30, 2023.
M. Hennequin, K. Benabdeslem and H. Elghazel. PAC-Bayesian domain adaptation bounds for multi-view learning, In the proceedings of the ICML Workshop on PAC-Bayes and Interactive Learning, Hawaii, USA, July 28th 2023.
G. Lefebvre, H. Elghazel, T. Guillet, A. Aussem and M. Sonnati. BERTEPro : A new Sentence Embedding Framework for the Education and Professional Training domain, In the proceedings of the 38th ACM/SIGAPP Symposium On Applied Computing (SAC 2023), Tallinn, Estonia, March 27-31, 2023.
L. Liekah, H. Elghazel, F. De Marchi and M-S. Hacid. Clustering Multivariate Longitudinal Data Application on Disease Progression Modeling, In the proceedings of the 20th International Conference on Modeling Decisions for Artificial Intelligence (MDAI 2023), Umea, Sweden, June 19-22, 2023.
A. Gaddari, G. Lefebvre, H. Elghazel, R. Jaziri, P-H. Combles and M. Sonnati. F-BERTMed : A new Sentence Embedding Framework for the French Medical domain, In the proceedings of the 7th International Workshop on Semantics-Powered Health Data Analytics (SEPDA 2023), Portoroz, Slovenia, June 12-15, 2023.
T. Ranvier, H. Elghazel, E. Coquery and K. Benabdeslem. Autoencoder-based Attribute Noise Handling Method for Medical Data, In the proceedings of the International Conference on Neural Information Processing (ICONIP 2022), New Delhi, India, November 22-26, 2022. IEEE Computer Society.
M. Hennequin, K. Benabdeslem, H. Elghazel, T. Ranvier and E. Michoux. Multi-view Self-Attention for Regression Domain Adaptation with feature selection, In the proceedings of the International Conference on Neural Information Processing (ICONIP 2022), New Delhi, India, November 22-26, 2022. IEEE Computer Society.
M. Hennequin, K. Benabdeslem and H. Elghazel. Adversarial Multi-View Domain Adaptation for Regression, In the proceedings of the International Joint Conference on Neural Network (IJCNN 2022), Padova, Italy, July 18-23, 2022. IEEE Computer Society.
H. Bertrand, H. Elghazel. S. Masmoudi, E. Coquery and M-S. Hacid. Localized Feature Ranking approach for Multi-Target Regression, In the proceedings of the International Joint Conference on Neural Network (IJCNN 2022), Padova, Italy, July 18-23, 2022. IEEE Computer Society.
C. Sage, T. Douzon, A. Aussem, V. Eglin, H. Elghazel, S. Duffner, C. Garcia and J. Espinas. Data-Efficient Information Extraction from Documents with Pre-Trained Language Models, In the proceedings of the DIL 2021 Workshop on Document Images and Language, Lausanne, Switzerland, September 6, 2021.
D. Boulegane, A. Bifet, H. Elghazel and G. Madhusudan. Streaming Time Series Forecasting using Multi-Target Regression with Dynamic Ensemble Selection, In the proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), Online, December 10-13, 2020. IEEE Computer Society, pages 2170-2179.
C. Sage, A. Aussem, V. Eglin, H. Elghazel and J. Espinas. End-to-End Extraction of Structured Information from Business Documents with Pointer-Generator Networks, In the proceedings of the EMNLP 2020 Workshop on Structured Prediction for NLP, Punta Cana (Online), Dominican Republic, November 20, 2020, pages 43-52.
O. Yazar, H. Elghazel, M-S. Hacid and N. Castin. Identifying Conditionally Independent Target Subsets for Multi-Target Regression, In the proceedings of the 32nd International Conference on Tools with Artificial Intelligence (ICTAI 2020), Online, November 9-11, 2020. IEEE Computer Society, pages 976-981.
H. Dabbechi, N. Zaaboub Haddar, H. Elghazel and K. Haddar. NoSQL Data Lake: A Big Data Source from Social Media, In the proceedings of the 20th International Conference on Hybrid Intelligent Systems (HIS 2020), Online, December 14-16, 2020. Lecture Notes in Computer Science - © Springer Verlag, pages 93-102.
H. Dabbechi, N. Zaaboub Haddar, H. Elghazel and K. Haddar. Social Media Data Integration: From Data Lake to NoSQL Data Warehouse, In the proceedings of the 20th International Conference on Intelligent Systems Design and Applications (ISDA 2020), Online, December 12-15, 2020. Lecture Notes in Computer Science - © Springer Verlag, pages 701-710.
O. Yazar, H. Elghazel, M-S. Hacid and N. Castin. Conditionally Decorrelated Multi-Target Regression, In the proceedings of the 26th International Conference on Neural Information Processing (ICONIP 2019), Sydney, Australia, December 12-15, 2019. Lecture Notes in Computer Science - © Springer Verlag, pages 445-457.
C. Sage, A. Aussem, H. Elghazel, V. Eglin and J. Espinas. Recurrent Neural Network Approach for Table Field Extraction in Business Documents, In the proceedings of the International Conference on Document Analysis and Recognition (ICDAR 2019), Sydney, Australia, September 20-25, 2019. IEEE Computer Society, pages 1308-1313.
A. Narassiguin, H. Elghazel and A. Aussem. Dynamic Ensemble Selection with Probabilistic Classifier Chains, In the proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2017), Skopje, Madcedonia, September 18-22, 2017. Lecture Notes in Computer Science - © Springer Verlag, pages 169-186.
O. Gharroudi, H. Elghazel and A. Aussem. A Semi-Supervised Ensemble Approach for Multi-label Learning, In the proceedings of ICDM Workshops, Barcelona, Spain, December 12-15, 2016. IEEE Computer Society, pages 1197-1204.
A. Narassiguin, H. Elghazel and A. Aussem. Similarity Tree Pruning: A Novel Dynamic Ensemble Selection Approach, In the proceedings of ICDM Workshops, Barcelona, Spain, December 12-15, 2016. IEEE Computer Society, pages 1243-1250.
R. Makkhongkaew, K. Benabdeslem and H. Elghazel. Semi-supervised co-selection: features and instances by a weighting approach, In the proceedings of the International Joint Conference on Neural Network (IJCNN 2016), Vancouver, Canada, July 24-29, 2016. IEEE Computer Society, pages 3477-3484.
M. Gasse, A. Aussem and H. Elghazel. On the Optimality of Multi-Label Classification under Subset Zero-One Loss for Distributions Satisfying the Composition Property, In the proceedings of the International Conference on Machine Learning (ICML 2015), Lille, France, July 6-11, 2015. JMLR Proceedings 37, pages 2531-2539.
O. Gharroudi, H. Elghazel, A. Aussem. Ensemble Multi-label Classification: A Comparative Study on Threshold Selection and Voting Methods, In the proceedings of International Conference on Tools with Artificial Intelligence (ICTAI 2015), Vietri sul Mare, Italy, November 9-11, 2015. IEEE Computer Society, pages 377-384.
O. Gharroudi, H. Elghazel, A. Aussem. Calibrated k-labelsets for Ensemble Multi-Label Classification, In the proceedings of International Conference on Neural Information Processing (ICONIP 2015), Istanbul, Turkey, November 9-12, 2015. Lecture Notes in Computer Science - © Springer Verlag, pages 573-582.
M. Gasse, A. Aussem and H. Elghazel. On the Factorization of the Label Conditional Distribution in the context of Multi-Label Classification, In the proceedings of International Workshop on Big Multi-Target Prediction, co-located with ECML PKDD 2015, Porto, Portugal, September 2015.
O. Gharroudi, H. Elghazel, A. Aussem. A Comparison of Multi-Label Feature Selection Methods Using the Random Forest Paradigm, In the proceedings of Canadian Conference on Artificial Intelligence (AI 2014), Montréal, Canada, Mai 6-9, 2014. Lecture Notes in Computer Science - © Springer Verlag, pages 95-106.
M. Bibimoune, H. Elghazel, A. Aussem. An Empirical Comparison of Supervised Ensemble Learning Approaches, In the proceedings of Workshop on Solving Complex Machine Learning Problems with Ensemble Methods (COPEM 2013), co-located with ECML PKDD 2013, Prague, September 2013, pages 123-138.
M. Hindawi, H. Elghazel, K. Benabdeslem. Efficient semi-supervised feature selection by an ensemble approach, In the proceedings of Workshop on Solving Complex Machine Learning Problems with Ensemble Methods (COPEM 2013), co-located with ECML PKDD 2013, Prague, September 2013, pages 41-55.
M. Gasse, A. Aussem, H. Elghazel. An experimental comparison of hybrid algorithms for Bayesian network structure learning, In the proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2012), Bristol, UK, September 24-28, 2012. Lecture Notes in Computer Science - © Springer Verlag, pages 58-73.
T-H. LE, H. Elghazel and M-S. Hacid. A Relational-based Approach for Aggregated Search in Graph Databases, In the proceedings of the International Conference on Database Systems for Advanced Applications (DASFAA 2012), Busan, South Korea, April 15-18, 2012. Lecture Notes in Computer Science - © Springer Verlag, pages 33-47. (acceptance rate for regular papers 27.6%)
H. Barkia, H. Elghazel and A. Aussem. Semi-Supervised Feature Importance Evaluation with Ensemble Learning, In the proceedings of IEEE International Conference on Data Mining (ICDM 2011), Vancouver, Canada, December 11-14, 2011. IEEE Computer Society, pages 31-40. (acceptance rate for regular papers 12%).
K. Benabdeslem, B. Effantin and H. Elghazel. A graph enrichment based clustering over vertically partitioned data, In the proceedings of the 7th International Conference on Advanced Data Mining and Applications (ADMA 2011), Beijing, China, December 17-19, 2011. Lecture Notes in Artificial Intelligence - © Springer Verlag- ISBN: 978-3-642-25852-7, pages 42-54.
N. Ben Aoun, H. Elghazel, M-S. Hacid and C. Ben Amar. Graph aggregation based image modeling and indexing for video annotation, In the 14th International Conference on Computer Analysis of Images and Patterns (CAIP 2011), Seville, Spain, August 29-31, 2011. Lecture Notes in Computer Science - © Springer Verlag - ISBN: 978-3-642-23677-8, pages 324-331.
H. Elghazel and M-S. Hacid. Aggregated Search in Graph Databases: Preliminary Results, In the 8th IAPR-TC-15 Workshop on Graph-based Representations in Pattern Recognition (GbR 2011), Munster, Germany, May 18-20, 2011. Lecture Notes in Computer Science - © Springer Verlag - ISBN: 978-3-642-20843-0, pages 92-101.
H. Elghazel and A. Aussem. Feature Selection for Unsupervised Learning using Random Cluster Ensembles, In the proceedings of IEEE International Conference on Data Mining (ICDM 2010), Sydney, Australia, 2010, IEEE Computer Society, pages 168-175. (acceptance rate for regular papers 9.03%).
H. Elghazel, A. Aussem and F. Perraud. Trading-off diversity and accuracy for optimal ensemble tree selection in random forests, In the proceedings of Workshop on Supervised and Unsupervised Ensemble Methods and their Applications (SUEMA 2010), co-located with ECML PKDD 2010, Barcelona, September 2010.
R. Jaziri, K. Benabdeslem and H. Elghazel. A Graph based framework for clustering and characterization of SOM, In the proceedings of 20th International Conference on Artificial Neural Networks (ICANN 2010), Thessaloniki, Greece, September 15-18, 2010. Lecture Notes in Computer Science Intelligence - © Springer Verlag - ISBN: 978-3-642-15824-7, pages 387-396.
H. Elghazel, K. Benabdeslem and F. Hamdi. Consensus clustering by graph based approach, In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2010), Bruges, Belgium, April 28-30, 2010.
H. Elghazel, K. Benabdeslem and Hamamache Kheddouci. McSOM: Minimal Coloring of Self Organizing Map, In the 5th International Conference on Advanced Data Mining and Applications (ADMA 2009), Beijing, China, August 17-19, 2009. Lecture Notes in Artificial Intelligence - © Springer Verlag - ISBN: 978-3-642-03347-6, pages 128-139. (acceptance rate 12%)
H. Elghazel and K. Benabdeslem. Towards b-coloring of SOM, In the 6th International Conference on Machine Learning and Data Mining (MLDM 2009), Leipzig, Germany, July 23-25, 2009. Lecture Notes in Artificial Intelligence - © Springer Verlag - ISBN: 978-3-642-03069-7, pages 322-336.
H. Elghazel, T. Yoshida, M-S. Hacid. An Integrated Graph and Probability Based Clustering Framework for Sequential Data, In the 11th International Conference on Discovery Science (DS 2008), Budapest, Hungary, October 13-16, 2008. Lecture Notes in Artificial Intelligence Nº5255 - © Springer Verlag - ISBN: 978-3-540-88410-1, pages 246-258.
H. Elghazel, V. Deslandres, K. Kallel and A. Dussauchoy. Clinical Pathway Analysis Using Graph-Based Approach and Markov Models, In the Second IEEE/ACM International Conference on Digital Information Management (ICDIM 2007), Lyon, France, October 28-31, 2007, IEEE Computer Press. ISBN: 978-1-4244-1476-8, pages 279-284.
H. Elghazel, H. Kheddouci, V. Deslandres and A. Dussauchoy. A Partially Dynamic Clustering Algorithm for Data Insertion and Removal, In the 10th International Conference on Discovery Science (DS 2007), Sendai, Japan, October 1-4, 2007. Lecture Notes in Artificial Intelligence Nº4755 - © Springer Verlag - ISBN: 978-3-540-75487-9, pages 78-90. (acceptance rate 30%)
H. Elghazel, K. Benabdeslem and A. Dussauchoy. Constrained Graph b-coloring based Clustering Approach, In the 9th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2007), Regensburg, Germany, September 3-7, 2007. Lecture Notes in Computer Science Nº4654 - © Springer Verlag - ISBN: 978-3-540-74552-5, pages 262-271. (acceptance rate 29%)
H. Elghazel, K. Benabdeslem and A. Dussauchoy. Clustering With Constraints Using Graph Based Approach, In the 5th International Workshop on Mining and Learning with Graphs (MLG 2007), Florence, Italy. August 1-3, 2007.
H. Elghazel, H. Kheddouci, V. Deslandres and A. Dussauchoy. A Graph-based Approach for Dynamic Clustering, In the 5th International Workshop on Mining and Learning with Graphs (MLG 2007), Florence, Italy. August 1-3, 2007.
V. Deslandres, H. Elghazel, K. Kallel and A. Dussauchoy. A decision support system for clinical pathways analysis, In the 33rd conference on Operational Research Applied to Health Service (ORAHS 2007), Saint-Etienne, France, July 15-20, 2007.
H. Elghazel, T. Yoshida, V. Deslandres, M-S. Hacid and A. Dussauchoy. A New Greedy Algorithm for improving b-Coloring Clustering, In 6th IAPR-TC-15 Workshop on Graph-based Representations in Pattern Recognition (GbR 2007), Alicante, Spain, June 11-13, 2007. Lecture Notes in Computer Science Nº4538 - © Springer Verlag - ISBN: 978-3-540-72902-0, pages 228-239.
H. Elghazel, V. Deslandres, M-S.. Hacid, A. Dussauchoy and H. Kheddouci. A New Clustering Approach for Symbolic Data and its Validation: Application to the Healthcare Data, In 16th International Symposium on Methodologies for Intelligent Systems (ISMIS 2006), Bari, Italie, September 27-29, 2006. Lecture Notes in Artificial Intelligence Nº4203 - © Springer Verlag - ISBN: 3-540-45764-X, pages 473-482. (acceptance rate 35%)
H. Elghazel, H. Kheddouci, V. Deslandres and A. Dussauchoy. A New Graph Based Clustering Approach: Application to PMSI Data, In the 3rd IEEE International Conference on Services Systems and Services Management (ICSSSM 2006), Troyes, France, October 25-27, 2006. ISBN: 1-4244-0450-9, pages 110-115.
H. Elghazel, K. Idrissi, A. Baskurt and C. Ben Amar. Textual Approaches for image retrieval (Approches textuelles pour la recherche l’images), In the 3rd IEEE International Conference on Sciences of Electronic, Technologies of Information and Telecommunications (SETIT 2005), Sousse, Tunisie, March 27-31, 2005. ISBN: 9973-51-546-3.
H. Elghazel, K. Idrissi, A. Baskurt and C. Ben Amar. Bacteria Analysis and identifications in the microscopic color images of biofilms (Analyse et Identification de Bactéries dans les Images Microscopiques couleur de Biofilms), In the first International Conference on Signals, Circuits & Systems (SCS 2004), Monastir, Tunisie, March 2004, pages 573-576.

• National Conferences

T. Ranvier, H. Elghazel, E. Coquery and K. Benabdeslem. Considération de l'incertitude de l'imputation dans l'apprentissage des réseaux de neurones, In Société Francophone de Classification (SFC 2023), Strasbourg, France, 06-07 juillet 2023.
M. Hennequin, K. Benabdeslem, and H. Elghazel. Des bornes PAC-Bayesiennes pour l'adaptation de domaine non supeervisée dans un cadre d'apprentissage multi-vues, In Société Francophone de Classification (SFC 2023), Strasbourg, France, 06-07 juillet 2023.
G. Lefebvre, H. Elghazel, T. Guillet, A. Aussem and M. Sonnati. BERTEPro : Une nouvelle approche de représentation sémantique dans le domaine de l'éducation et de la formation professionnelle , In the proceedings of the Extraction et Gestion des Connaissances (EGC 2023), Lyon, France, 16-29 Janvier, 2023.
A. Gaddari, H. Elghazel, R. Jaziri, M-S. Hacid and P-H. Combles. Classification multi-label de données médicales par LSTM temporel et clustering flou, In the proceedings of the Extraction et Gestion des Connaissances (EGC 2023), Lyon, France, 16-29 Janvier, 2023.
R. Makkhongkaew, K. Benabdeslem and H. Elghazel. Co-sélection instances-variables en mode semi-supervisé, In Société Francophone de Classification (SFC 2016), Marrakech, Maroc, 22-26 Mai, 2016.
F. Hamdi, H. Elghazel and K.Benabdeslem. Approche graphique pour l'agrégation de classifications non-supervisées, Atelier : Fouille de données complexes dans le cadre de la conférence EGC2010 (AFDC-EGC 2010), Hammamet, Janvier, 2010.
H. Elghazel and K. Benabdeslem. Segmentation graphique des cartes topologiques, In 11ème Conférence Francophone sur l’apprentissage automatique (CAp 2009), Plate-forme AFIA, Hammamet, Tunisie, 26-29 Mai, 2009. Editions Cépaduès.
H. Elghazel, H. Kheddouci, V. Deslandres and A. Dussauchoy. Une approche incrémentale de classification non supervisée par b-coloration de graphes, In 9ème Conférence Francophone sur l’apprentissage automatique (CAp 2007), Plate-forme AFIA, Grenoble, France, 2-6 Juillet, 2007. Editions Cépaduès. ISBN: 978-2-85428-789-9, pages 107-122. (acceptance rate 40%)
T. Yoshida, H. Elghazel, V. Deslandres, M-S. Hacid and A. Dussauchoy. A Re-Coloring Method for b-coloring based Clustering, In the 18th IEICE Data Engineering Workshop in conjunction with the 5th Annual Conference on the Database Society of Japan (DBSJ). Hiroshima, Japan. 28th February - 2nd March, 2007. (In Japanese).
H. Elghazel, M-S. Hacid, H. Kheddouci and A. Dussauchoy. Une nouvelle approche de clustering pour les données symboliques: Algorithmes et Application aux Données Médicales (A New Clustering Approach for Symbolic Data: Algorithms and Application to Healthcare Data), In 22ème journées de Bases de données Avancées (BDA 2006), Lille, France, 17-20 Octobre, 2006. (acceptance rate 29%)
H. Elghazel, V. Deslandres and A. Dussauchoy. Analyse de données PMSI : Etude des groupes homogènes de malades et proposition l’une nouvelle typologie de séjours, In 3ème conférence francophone en gestion et ingénierie des systèmes hospitaliers (GISEH 2006), Luxembourg, 14-16 Septembre, 2006.

• Demonstrations

M. Azzaoui, J. Legrand and H. Elghazel. KSyDoC: A Keyword-based System for DOcument Clustering and Retrieval, In 23ème journées de Bases de données Avancées (BDA 2007), Marseille, France, 23-26 Octobre, 2007.

• Research Reports

H. Elghazel. Initiation au Programme de Médicalisation des Systèmes l’Information (PMSI) Français, Rapport interne, Laboratoire PRISMa, Université Claude Bernard Lyon 1, France. Février, 2005. [Report]
H. Elghazel. Approches textuelles pour la recherche l’images, Mémoire de DEA, Institut National des Sciences Appliquées de Lyon, France. Juillet, 2004. [Thesis]
H. Elghazel. Analyse et identifications de bactéries dans les images microscopiques couleur de biofilms, Mémoire de Projet de Fin d’études, Ecole Nationale l’Ingénieurs de Sfax (Tunisie) et Laboratoire l’InfoRmatique en Images et Systèmes l’informations LIRIS de l’Université Claude Bernard Lyon 1 (France). Juin, 2003. [Thesis]