Neural Representation and Learning of Hierarchical 2-additive Choquet Integrals
| Autor: | Christophe Labreuche, Michèle Sebag, Johanne Cohen, Eyke Hüllermeier, Roman Bresson |
|---|---|
| Přispěvatelé: | Graphes, Algorithmes et Combinatoire (LRI) (GALaC - LRI), Laboratoire de Recherche en Informatique (LRI), CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Thales Research and Technology [Palaiseau], THALES [France], CentraleSupélec-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Universität Paderborn (UPB), TAckling the Underspecified (TAU), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de Recherche en Informatique (LRI), THALES, University of Paderborn |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Preference Modelling and Preference-Based Reasoning 0211 other engineering and technologies [SCCO.COMP]Cognitive science/Computer science 02 engineering and technology Machine learning computer.software_genre [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] 0202 electrical engineering electronic engineering information engineering [INFO]Computer Science [cs] Learning Preferences or Rankings Preference Modelling and Preference-Based Reasoning Utility Theory Knowledge-based Learning Representation (mathematics) ComputingMilieux_MISCELLANEOUS Interpretability 021103 operations research Learning Preferences or Rankings business.industry Utility Theory [INFO.INFO-RO]Computer Science [cs]/Operations Research [cs.RO] Multiple-criteria decision analysis Identification (information) Knowledge-based Learning Choquet integral 020201 artificial intelligence & image processing Artificial intelligence Marginal utility business computer |
| Zdroj: | Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {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⟩ IJCAI-PRICAI-20-Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence 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⟩ IJCAI |
| DOI: | 10.24963/ijcai.2020/275⟩ |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
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