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Theses

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

Abstract : 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.
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Submitted on : Thursday, April 25, 2019 - 4:37:10 PM
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Khalil 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. Intelligence artificielle [cs.AI]. Sorbonne Université; Université Hassan II (Mohammedia, Maroc). Faculté des sciences et techniques, 2018. Français. ⟨NNT : 2018SORUS032⟩. ⟨tel-02110974⟩

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