Fonctions latticielles polynomiales pour l’interpolation et la classification monotone

Quentin Brabant 1
1 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : A Lattice Polynomial Function (LPF) over a lattice L is a map p : Ln → L that can be defined by an expression involving variables, constants and the lattice operators ∧ and ∨. If L is a distributive lattice, these maps include the so-called Sugeno integrals that are aggregation functions capable of merging ordinal values, not necessarily numerical. They are widely used in the qualitative approach to Multiple Criteria Decision Aiding (MCDA), and they can be thought of as the ordinal counterparts of Choquet integrals. In the first part of this thesis, we tackle the task of interpolating a partial function by an LPF, stated as follows: for a lattice L, a finite subset D of Ln, and a function f : D → L, return an LPF p : Ln → L such that p(x) = f(x) for all x ∊ D (if such an LPF exists). We treat the cases where L is a finite lattice, and then the cases where L is a bounded distributive lattice. In both cases, we provide algorithms that solve this problem in polynomial time. In the second part, we consider generalizations of Sugeno integrals in the multi-attribute setting, in particular, the Sugeno Utility Functions (SUFs), that are able to merge values coming from different ordinal scales. We consider the their use in monotonic classification tasks. We present a model based on a set of SUFs and an algorithm for learning such model from data. We compare this model to the sets of monotonic decision rules learned by VC-DomLEM, and study the number of SUFs that are required in order to model empirical data
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Quentin Brabant. Fonctions latticielles polynomiales pour l’interpolation et la classification monotone. Intelligence artificielle [cs.AI]. Université de Lorraine, 2019. Français. ⟨NNT : 2019LORR0006⟩. ⟨tel-02096400⟩

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