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Haytham Elghazel
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2020 – today
- 2024
- [j9]Guillaume Lefebvre
, Haytham Elghazel
A new sentence embedding framework for the education and professional training domain with application to hierarchical multi-label text classification. Data Knowl. Eng. 150: 102281 (2024) - [i4]Mehdi Hennequin, Khalid Benabdeslem, Haytham Elghazel:
PAC-Bayesian Domain Adaptation Bounds for Multi-view learning. CoRR abs/2401.01048 (2024) - 2023
- [c43]Abdelhamid Gaddari, Haytham Elghazel, Rakia Jaziri, Mohand-Said Hacid, Pierre-Henri Comble:
A New Time-Aware LSTM based Framework for Multi-label Classification on Healthcare Data. AICCSA 2023: 1-8 - [c42]Thomas Ranvier
Accounting for Imputation Uncertainty During Neural Network Training. DaWaK 2023: 265-280 - [c41]Guillaume Lefebvre, Haytham Elghazel, Théodore Guillet, Alexandre Aussem, Matthieu Sonnati:
BERTEPro : Une nouvelle approche de représentation sémantique dans le domaine de l'éducation et de la formation professionnelle. EGC 2023: 211-222 - [c40]Abdelhamid Gaddari, Haytham Elghazel, Rakia Jaziri, Mohand-Saïd Hacid, Pierre-Henri Comble:
Classification multi-label de données médicales par LSTM temporel et clustering flou. EGC 2023: 369-376 - [c39]Guillaume Lefebvre
BERTEPro : A new Sentence Embedding Framework for the Education and Professional Training domain. SAC 2023: 929-935 - 2022
- [c38]Mehdi Hennequin
Multi-view Self-attention for Regression Domain Adaptation with Feature Selection. ICONIP (1) 2022: 177-188 - [c37]Thomas Ranvier
Autoencoder-Based Attribute Noise Handling Method for Medical Data. ICONIP (6) 2022: 212-223 - [c36]Hugo Bertrand, Haytham Elghazel, Sahar Masmoudi, Emmanuel Coquery, Mohand-Saïd Hacid:
Localized Feature Ranking approach for Multi-Target Regression. IJCNN 2022: 1-8 - [c35]Mehdi Hennequin
Adversarial Multi - View Domain Adaptation for Regression. IJCNN 2022: 1-8 - [i3]Thomas Ranvier, Haytham Elghazel, Emmanuel Coquery, Khalid Benabdeslem:
Autoencoder-based Attribute Noise Handling Method for Medical Data. CoRR abs/2206.10609 (2022) - 2021
- [c34]Clément Sage, Thibault Douzon, Alex Aussem, Véronique Eglin, Haytham Elghazel, Stefan Duffner, Christophe Garcia, Jérémy Espinas:
Data-Efficient Information Extraction from Documents with Pre-trained Language Models. ICDAR Workshops (2) 2021: 455-469 - 2020
- [c33]Clément Sage, Alex Aussem, Véronique Eglin, Haytham Elghazel, Jérémy Espinas:
End-to-End Extraction of Structured Information from Business Documents with Pointer-Generator Networks. SPNLP@EMNLP 2020: 43-52 - [c32]Dihia Boulegane, Albert Bifet
Streaming Time Series Forecasting using Multi-Target Regression with Dynamic Ensemble Selection. IEEE BigData 2020: 2170-2179 - [c31]Hichem Dabbèchi
NoSQL Data Lake: A Big Data Source from Social Media. HIS 2020: 93-102 - [c30]Orhan Yazar, Haytham Elghazel, Mohand-Saïd Hacid, Nathalie Castin:
Identifying Conditionally Independent Target Subsets for Multi-Target Regression*Note: Sub-titles are not captured in Xplore and should not be used. ICTAI 2020: 976-981 - [c29]Hichem Dabbèchi
Social Media Data Integration: From Data Lake to NoSQL Data Warehouse. ISDA 2020: 701-710
2010 – 2019
- 2019
- [c28]Clément Sage, Alexandre Aussem, Haytham Elghazel, Véronique Eglin, Jérémy Espinas:
Recurrent Neural Network Approach for Table Field Extraction in Business Documents. ICDAR 2019: 1308-1313 - [c27]Orhan Yazar, Haytham Elghazel, Mohand-Said Hacid, Nathalie Castin:
Conditionally Decorrelated Multi-Target Regression. ICONIP (2) 2019: 445-457 - 2017
- [c26]Anil Narassiguin, Haytham Elghazel, Alex Aussem:
Dynamic Ensemble Selection with Probabilistic Classifier Chains. ECML/PKDD (1) 2017: 169-186 - 2016
- [j8]Haytham Elghazel, Alex Aussem, Ouadie Gharroudi, Wafa Saadaoui:
Ensemble multi-label text categorization based on rotation forest and latent semantic indexing. Expert Syst. Appl. 57: 1-11 (2016) - [j7]Khalid Benabdeslem, Haytham Elghazel, Mohammed M. Al-Hindawi
Ensemble constrained Laplacian score for efficient and robust semi-supervised feature selection. Knowl. Inf. Syst. 49(3): 1161-1185 (2016) - [j6]Anil Narassiguin, Mohamed Bibimoune, Haytham Elghazel, Alex Aussem:
An extensive empirical comparison of ensemble learning methods for binary classification. Pattern Anal. Appl. 19(4): 1093-1128 (2016) - [c25]Ouadie Gharroudi, Haytham Elghazel, Alexandre Aussem:
A Semi-Supervised Ensemble Approach for Multi-label Learning. ICDM Workshops 2016: 1197-1204 - [c24]Anil Narassiguin, Haytham Elghazel, Alex Aussem:
Similarity Tree Pruning: A Novel Dynamic Ensemble Selection Approach. ICDM Workshops 2016: 1243-1250 - [c23]Raywat Makkhongkaew, Khalid Benabdeslem, Haytham Elghazel:
Semi-supervised co-selection: Features and instances by a weighting approach. IJCNN 2016: 3477-3484 - 2015
- [j5]Haytham Elghazel, Alex Aussem:
Unsupervised feature selection with ensemble learning. Mach. Learn. 98(1-2): 157-180 (2015) - [c22]Maxime Gasse, Alexandre Aussem, Haytham Elghazel:
On the Optimality of Multi-Label Classification under Subset Zero-One Loss for Distributions Satisfying the Composition Property. ICML 2015: 2531-2539 - [c21]Ouadie Gharroudi, Haytham Elghazel, Alex Aussem:
Calibrated k-labelsets for Ensemble Multi-label Classification. ICONIP (1) 2015: 573-582 - [c20]Ouadie Gharroudi, Haytham Elghazel, Alex Aussem:
Ensemble Multi-label Classification: A Comparative Study on Threshold Selection and Voting Methods. ICTAI 2015: 377-384 - [i2]Maxime Gasse, Alex Aussem, Haytham Elghazel:
An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning. CoRR abs/1505.05004 (2015) - [i1]Maxime Gasse, Alex Aussem, Haytham Elghazel:
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning. CoRR abs/1506.05692 (2015) - 2014
- [j4]Maxime Gasse, Alex Aussem, Haytham Elghazel:
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning. Expert Syst. Appl. 41(15): 6755-6772 (2014) - [j3]Haytham Elghazel, Khalid Benabdeslem:
Different Aspects of Clustering The Self-Organizing Maps. Neural Process. Lett. 39(1): 97-114 (2014) - [c19]Ouadie Gharroudi, Haytham Elghazel, Alex Aussem:
A Comparison of Multi-Label Feature Selection Methods Using the Random Forest Paradigm. Canadian AI 2014: 95-106 - 2012
- [j2]Fazia Bellal, Haytham Elghazel, Alex Aussem:
A semi-supervised feature ranking method with ensemble learning. Pattern Recognit. Lett. 33(10): 1426-1432 (2012) - [c18]Thanh-Huy Le, Haytham Elghazel, Mohand-Said Hacid:
A Relational-Based Approach for Aggregated Search in Graph Databases. DASFAA (1) 2012: 33-47 - [c17]Maxime Gasse
An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning. ECML/PKDD (1) 2012: 58-73 - 2011
- [c16]Khalid Benabdeslem, Brice Effantin
A Graph Enrichment Based Clustering over Vertically Partitioned Data. ADMA (1) 2011: 42-54 - [c15]Najib Ben Aoun
Graph Aggregation Based Image Modeling and Indexing for Video Annotation. CAIP (2) 2011: 324-331 - [c14]Haytham Elghazel, Mohand-Said Hacid:
Aggregated Search in Graph Databases: Preliminary Results. GbRPR 2011: 92-101 - [c13]Hasna Barkia, Haytham Elghazel, Alex Aussem:
Semi-supervised Feature Importance Evaluation with Ensemble Learning. ICDM 2011: 31-40 - [p1]Haytham Elghazel, Alex Aussem, Florence Perraud:
Trading-Off Diversity and Accuracy for Optimal Ensemble Tree Selection in Random Forests. Ensembles in Machine Learning Applications 2011: 169-179 - 2010
- [c12]Haytham Elghazel, Khalid Benabdeslem, Fatma Hamdi:
Consensus clustering by graph based approach. ESANN 2010 - [c11]Rakia Jaziri, Khalid Benabdeslem, Haytham Elghazel:
A Graph Based Framework for Clustering and Characterization of SOM. ICANN (3) 2010: 387-396 - [c10]Haytham Elghazel, Alex Aussem:
Feature Selection for Unsupervised Learning Using Random Cluster Ensembles. ICDM 2010: 168-175
2000 – 2009
- 2009
- [c9]Haytham Elghazel, Khalid Benabdeslem, Hamamache Kheddouci:
McSOM: Minimal Coloring of Self-Organizing Map. ADMA 2009: 128-139 - [c8]Haytham Elghazel, Khalid Benabdeslem:
Towards B-Coloring of SOM. MLDM 2009: 322-336 - 2008
- [j1]Haytham Elghazel, Hamamache Kheddouci, Véronique Deslandres, Alain Dussauchoy:
A Graph b-coloring Framework for Data Clustering. J. Math. Model. Algorithms 7(4): 389-423 (2008) - [c7]Haytham Elghazel, Tetsuya Yoshida, Mohand-Said Hacid:
An Integrated Graph and Probability Based Clustering Framework for Sequential Data. Discovery Science 2008: 246-258 - 2007
- [c6]Haytham Elghazel, Khalid Benabdeslem, Alain Dussauchoy:
Constrained Graph b-Coloring Based Clustering Approach. DaWaK 2007: 262-271 - [c5]Haytham Elghazel, Hamamache Kheddouci, Véronique Deslandres, Alain Dussauchoy:
A Partially Dynamic Clustering Algorithm for Data Insertion and Removal. Discovery Science 2007: 78-90 - [c4]Haytham Elghazel, Tetsuya Yoshida, Véronique Deslandres, Mohand-Said Hacid, Alain Dussauchoy:
A New Greedy Algorithm for Improving b-Coloring Clustering. GbRPR 2007: 228-239 - [c3]Haytham Elghazel, Véronique Deslandres, Kassem Kallel, Alain Dussauchoy:
Clinical pathway analysis using graph-based approach and Markov models. ICDIM 2007: 279-284 - 2006
- [c2]Haytham Elghazel, Mohand-Said Hacid, Hamamache Kheddouci, Alain Dussauchoy:
A New Clustering Approach for Symbolic Data: Algorithms and Application to Healthcare Data. BDA 2006 - [c1]Haytham Elghazel, Véronique Deslandres, Mohand-Said Hacid, Alain Dussauchoy, Hamamache Kheddouci:
A New Clustering Approach for Symbolic Data and Its Validation: Application to the Healthcare Data. ISMIS 2006: 473-482
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last updated on 2024-11-07 21:37 CET by the dblp team
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