How to Handle Missing Values in Multi-Criteria Decision Aiding?
Authors: Christophe Labreuche, Sébastien Destercke
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Axiomatic characterizations are proposed for three classes of models. For general quantitative models, the restriction operator is characterized by linearity, recursivity and decomposition on variables. The second class is the set of monotone quantitative models satisfying normalization conditions. The linearity axiom is changed to fit with these conditions. Adding recursivity and symmetry, the restriction operator takes the form of a normalized average. For the last class of models namely the Choquet integral, we obtain a simpler expression. Finally, a very intuitive interpretation is provided for this last model. |
| Researcher Affiliation | Collaboration | Christophe Labreuche1 , S ebastien Destercke2 1Thales Research & Technology, Palaiseau, France 2UMR CNRS 7253 Heudiasyc, Sorbonne universit es, Universit e de Technologie de Compi egne, Compi egne, France christophe.labreuche@thalesgroup.com, sebastien.destercke@hds.utc.fr |
| Pseudocode | No | The paper does not contain pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the methodology described. |
| Open Datasets | No | The paper focuses on theoretical developments and does not use or describe a dataset for training. |
| Dataset Splits | No | The paper focuses on theoretical developments and does not specify training/validation/test dataset splits. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for any experiments, as it is a theoretical paper. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies, as it is a theoretical paper and does not describe an experimental setup. |
| Experiment Setup | No | The paper focuses on theoretical derivations and does not describe an experimental setup with hyperparameters or training settings. |