Modeling and Learning of Hierarchical Decision Models: The Case of the Choquet Integral

Hüllermeier, Eyke (31. May 2022): Modeling and Learning of Hierarchical Decision Models: The Case of the Choquet Integral. In: Greco, Salvatore; Mousseau, Vincent; Stefanowski, Jerzy und Zopounidis, Constantin (eds.) : Intelligent Decision Support Systems: Combining Operations Research and Artificial Intelligence. Multiple Criteria Decision Making (MCDM), 1. Springer. pp. 101-120

Abstract

In this paper, we elaborate on two important developments in the realm of multi-criteria decision aid, which have attracted increasing attention in the recent past: first, the idea of leveraging methods from preference learning for the data-driven (instead of human-centric) construction of decision models, and second, the use of hierarchical instead of “flat” decision models. In particular, we show the advantage of combining the two, that is, of learning hierarchical MCDA models from suitable training data. This approach is illustrated by means of a concrete example, namely the learning of tree-structured combinations of the Choquet integral as a versatile aggregation function.

• Item Type: Book Section
• Faculties: Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning
• Subjects: 000 Computer science, information and general works > 004 Data processing computer science
• ISBN: 978-3-030-96318-7
• ISSN: 2366-0031