Neural Representation and Learning of Hierarchical 2-additive Choquet Integrals - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2020

Neural Representation and Learning of Hierarchical 2-additive Choquet Integrals

Résumé

Multi-Criteria Decision Making (MCDM) aims at modelling expert preferences and assisting decision makers in identifying options best accommodating expert criteria. An instance of MCDM model, the Choquet integral is widely used in real-world applications, due to its ability to capture interactions between criteria while retaining interpretability. Aimed at a better scalability and modularity, hierarchical Choquet integrals involve intermediate aggregations of the interacting criteria, at the cost of a more complex elicitation. The paper presents a machine learning-based approach for the automatic identification of hierarchical MCDM models, composed of 2-additive Choquet integral aggregators and of marginal utility functions on the raw features from data reflecting expert preferences. The proposed NEUR-HCI framework relies on a specific neural architecture, enforcing by design the Choquet model constraints and supporting its end-to-end training. The empirical validation of NEUR-HCI on real-world and artificial benchmarks demonstrates the merits of the approach compared to state-of-art baselines.
Fichier principal
Vignette du fichier
IJCAIBresson20.pdf (19.85 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03017078 , version 1 (20-11-2020)

Identifiants

Citer

Roman Bresson, Johanne Cohen, Eyke Hüllermeier, Christophe Labreuche, Michele Sebag. Neural Representation and Learning of Hierarchical 2-additive Choquet Integrals. IJCAI-PRICAI-20 - Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence, Jul 2020, Yokohama, France. pp.1984-1991, ⟨10.24963/ijcai.2020/275⟩. ⟨hal-03017078⟩
207 Consultations
19 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More