Holistic Preference Learning with the Choquet Integral
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- 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.
- © 2013, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
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 -