Preference disaggregation and statistical learning for multicriteria decision support: A review. (English) Zbl 1205.90147
Summary: Disaggregation methods have become popular in multicriteria decision aiding (MCDA) for eliciting preferential information and constructing decision models from decision examples. From a statistical point of view, data mining and machine learning are also involved with similar problems, mainly with regard to identifying patterns and extracting knowledge from data. Recent research has also focused on the introduction of specific domain knowledge in machine learning algorithms. Thus, the connections between disaggregation methods in MCDA and traditional machine learning tools are becoming stronger. In this paper the relationships between the two fields are explored. The differences and similarities between the two approaches are identified, and a review is given regarding the integration of the two fields.
MSC:
| 90B50 | Management decision making, including multiple objectives |
| 68T05 | Learning and adaptive systems in artificial intelligence |
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