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Neural Representation and Learning of Hierarchical 2-additive Choquet Integrals
- Source :
- 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
- Publication Year :
- 2020
- Publisher :
- HAL CCSD, 2020.
-
Abstract
- 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.
- Subjects :
- 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
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- 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
- Accession number :
- edsair.doi.dedup.....6635cbb302f4db0b03bb7a68f284572f