Holistic Preference Learning with the Choquet Integral
Bénédicte Goujon, Christophe Labreuche
Available Online August 2013.
- https://doi.org/10.2991/eusflat.2013.13How to use a DOI?
- Preference Learning Multi-criteria Decision Model Choquet Integral Fixed-point
- The current approaches to construct a multi-criteria model based on a Choquet integral are split into two separate steps: construct first the utility functions and then the aggregation function. Unfortunately, the decision maker may feel some difficulties in addressing these tricky steps. In this paper, we propose a preference learning algorithm that construct both the utility functions and the capacity from several preferences or evaluations. The algorithm is based on a fixed-point approach that transforms the global optimization learning problem into two iterative linear problems. Each problem objective is to minimize the number of non validated learning examples.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Bénédicte Goujon AU - Christophe Labreuche PY - 2013/08 DA - 2013/08 TI - Holistic Preference Learning with the Choquet Integral BT - Proceedings of the 8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-13) PB - Atlantis Press SP - 88 EP - 95 SN - 1951-6851 UR - https://doi.org/10.2991/eusflat.2013.13 DO - https://doi.org/10.2991/eusflat.2013.13 ID - Goujon2013/08 ER -