Algorithmes efficaces pour l’apprentissage de réseaux de préférences conditionnelles à partir de données bruitées

Abstract : The rapid growth of personal web data has motivated the emergence of learning algorithms well suited to capture users’ preferences. Among preference representation formalisms, conditional preference networks (CP-nets) have proven to be effective due to their compact and explainable structure. However, their learning is difficult due to their combinatorial nature.In this thesis, we tackle the problem of learning CP-nets from corrupted large datasets. Three new algorithms are introduced and studied on both synthetic and real datasets.The first algorithm is based on query learning and considers the contradictions between multiple users’ preferences by searching in a principled way the variables that affect the preferences. The second algorithm relies on information-theoretic measures defined over the induced preference rules, which allow us to deal with corrupted data. An online version of this algorithm is also provided, by exploiting the McDiarmid's bound to define an asymptotically optimal decision criterion for selecting the best conditioned variable and hence allowing to deal with possibly infinite data streams.
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Fabien Labernia. Algorithmes efficaces pour l’apprentissage de réseaux de préférences conditionnelles à partir de données bruitées. Intelligence artificielle [cs.AI]. PSL Research University, 2018. Français. ⟨NNT : 2018PSLED018⟩. ⟨tel-01898355⟩

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