H. Elghazel

发表

Toward Improving b-Coloring based Clustering using a Greedy re-Coloring Algorithm

Mohand-Said Hacid, A. Dussauchoy, Tetsuya Yoshida, 2008 .

A Genome-Wide Association Study Identifies a Novel Locus for Bortezomib-Induced Peripheral Neuropathy in European Multiple Myeloma Patients

Ludovic, P. Sonneveld, A. Aussem, 2020 .

Autoencoder-based Attribute Noise Handling Method for Medical Data

K. Benabdeslem, E. Coquery, H. Elghazel, 2022, ArXiv.

Adversarial Multi - View Domain Adaptation for Regression

K. Benabdeslem, H. Elghazel, Mehdi Hennequin, 2022, 2022 International Joint Conference on Neural Networks (IJCNN).

Unsupervised feature selection with ensemble learning

A. Aussem, H. Elghazel, 2013, Machine Learning.

Conditionally Decorrelated Multi-Target Regression

Mohand-Said Hacid, Haytham Elghazel, Orhan Yazar, 2019, ICONIP.

A Graph b-coloring Framework for Data Clustering

Hamamache Kheddouci, Haytham Elghazel, Véronique Deslandres, 2008, J. Math. Model. Algorithms.

A New Greedy Algorithm for Improving b-Coloring Clustering

Mohand-Said Hacid, Haytham Elghazel, Véronique Deslandres, 2007, GbRPR.

A New Graph-Based Clustering Approach: Application to PMSI Data

A. Dussauchoy, H. Kheddouci, A. Dussauchoy, 2006, 2006 International Conference on Service Systems and Service Management.

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

Mohand-Said Hacid, Hamamache Kheddouci, Haytham Elghazel, 2006, ISMIS.

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

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

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).

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

Raywat Makkhongkaew, Khalid Benabdeslem, Haytham Elghazel, 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

A Genome-Wide Association Study Identifies a Novel Locus for Bortezomib-Induced Peripheral Neuropathy in European Patients with Multiple Myeloma

P. Sonneveld, N. Munshi, A. Aussem, 2016, Clinical Cancer Research.

A Genome-Wide Association Study Identifies a Novel Locus for Bortezomib-Induced Peripheral Neuropathy in European Multiple Myeloma Patients Running title : GWAS in multiple myeloma pharmacogenomics

P. Sonneveld, N. Munshi, A. Aussem, 2016 .

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

Khalid Benabdeslem, Haytham Elghazel, Mohammed Hindawi, 2015, Knowledge and Information Systems.

Efficient semi-supervised feature selection by an ensemble approach

Haytham Elghazel, Mohammed Hindawi, Khalid Benabdeslem, 2013 .

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

Christophe Garcia, S. Duffner, A. Aussem, 2021 .

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

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

A machine-learning framework for predicting multiple air pollutants' concentrations via multi-target regression and feature selection.

Haytham Elghazel, Orhan Yazar, Amjad Kallel, 2020, The Science of the total environment.

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

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

A Relational-Based Approach for Aggregated Search in Graph Databases

Mohand-Said Hacid, Haytham Elghazel, Thanh-Huy Le, 2012, DASFAA.

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

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

A. Bifet, H. Elghazel, G. Madhusudan, 2020, 2020 IEEE International Conference on Big Data (Big Data).

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).

Graph modeling based video event detection

Haytham Elghazel, Chokri Ben Amar, Najib Ben Aoun, 2011, 2011 International Conference on Innovations in Information Technology.

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

Haytham Elghazel, Maxime Gasse, Alex Aussem, 2014, Expert Syst. Appl..

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

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

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).

A semi-supervised feature ranking method with ensemble learning

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

Localized Feature Ranking approach for Multi-Target Regression

E. Coquery, Mohand-Saïd Hacid, H. Elghazel, 2022, IEEE International Joint Conference on Neural Network.

Dynamic Ensemble Selection with Probabilistic Classifier Chains

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

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

A. Aussem, H. Elghazel, Maxime Gasse, 2014, Expert Syst. Appl..

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

Haytham Elghazel, Alex Aussem, Maxime Gasse, 2015, ICML.

An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning

Haytham Elghazel, Alex Aussem, Maxime Gasse, 2012, ECML/PKDD.

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

A. Aussem, H. Elghazel, Matthieu Sonnati, 2023, SAC.

Clinical pathway analysis using graph-based approach and Markov models

Haytham Elghazel, Véronique Deslandres, Alain Dussauchoy, 2007, 2007 2nd International Conference on Digital Information Management.

A Graph Based Framework for Clustering and Characterization of SOM

Khalid Benabdeslem, Haytham Elghazel, Rakia Jaziri, 2010, ICANN.

Unsupervised feature selection with ensemble learning

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

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

Mohand-Saïd Hacid, Haytham Elghazel, Orhan Yazar, 2020, 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI).

Towards B-Coloring of SOM

Khalid Benabdeslem, Haytham Elghazel, K. Benabdeslem, 2009, MLDM.

An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning

A. Aussem, H. Elghazel, Maxime Gasse, 2012, ECML/PKDD.

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.