Supervision : Christophe MARSALA
Co-supervision : RAMDANI Mohammed (FSTM Université Hassan II, Maroc)
Multi-class classification in big data
In graded multi-label classification (GMLC), each instance is associated to a set of labels with graded membership degrees. For example, the same odorous molecule may be associated to a strong 'musky' odor, a moderate 'animal' odor, and a weak 'grassy' odor. The goal is to learn a model to predict the graded set of labels associated to an instance from its descriptive variables. For example, predict the graduated set of odors from the molecular weight, the number of double bonds, and the structure of the molecule.
Another interesting area of the GMLC is recommendation systems. In fact, users' assessments of items (products, services, books, films, etc.) are first collected in the form of GML data (using the one-to-five star rating). These data are then used to recommend to each user items that are most likely to interest him.
In this thesis, an in-depth theoretical study of the GMLC allows to highlight the limits of existing approaches, and to introduce a set of new approaches bringing improvements evaluated experimentally on real data. The main point of the new proposed approaches is the exploitation of relations between labels. For example, a molecule with a strong 'musky' odor often has a weak or moderate 'animal' odor. This thesis also proposes new approaches adapted to the case of odorous molecules and to the case of large volumes of data collected in the context of recommendation systems.
Defence : 03/23/2018 - 14h30 - FSTM-Université Hassan II (Mohammadia, Maroc)
Jury members :
Abdelaziz BERRADO, Professeu, EMI-Université Mohammed V de Rabat [Rapporteur]
Anne LAURENT, Professeur, LIRMM, Université de Montpellier [Rapporteur]
Abdelkrim BEKKHOUCHA, Professeur, FSTM-Université HASSAN II de Casablanca
Bernadette BOUCHON-MEUNIER, Directrice de recherches émérite, LIP6, CNRS, Sorbonne Université, Paris
Christophe MARSALA, Professeur, LIP6, Sorbonne Université, Paris
Mohammed RAMDANI, FSTM-Université HASSAN II de Casablanca
- Kh. Laghmari : “Classification multi-labels graduée : découverte des relations entre les labels, et adaptation à la reconnaissance des odeurs et au contexte big data des systèmes de recommandation”, thesis, defence 03/23/2018, supervision Marsala, Christophe, rapporteurs : RAMDANI Mohammed (FSTM Université Hassan II, Maroc) (2018)
- kh. Laghmari, Ch. Marsala, M. Ramdani : “An adapted incremental graded multi-label classification model for recommendation systems”, Progress in Artificial Intelligence, (Springer) (2017)
- kh. Laghmari, Ch. Marsala, M. Ramdani : “A Distributed Recommender System Based on Graded Multi-label Classification”, International Conference on Networked Systems, vol. 10299, Lecture Notes in Computer Science, Marrakech, Morocco, pp. 101-108, (Springer) (2017)
- kh. Laghmari, Ch. Marsala, M. Ramdani : “Classification multi-labels graduée: Apprendre les relations entre les labels ou limiter la propagation d’erreur ?”, Actes EGC 2017, Grenoble, France (2017)
- kh. Laghmari, Ch. Marsala, M. Ramdani : “Graded multi-label classification: compromise between handling label relations and limiting error propagation”, SITA 2016 - 11th International Conference on Intelligent Systems: Theories and Applications, Mohammadia, Morocco, pp. 1-6, (IEEE) (2016)
- kh. Laghmari, M. Ramdani, Ch. Marsala : “A Distributed Graph Based Approach for Rough Classifications Considering Dominance Relations Between Overlapping Classes”, 2015 10th International Conference on Intelligent Systems: Theories and Applications (SITA), Rabat, Morocco, pp. 1-6, (IEEE) (2015)