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Kernel-based learning methods for preference aggregation

Content Provider	Springer Nature Link
Author	Waegeman, Willem Baets, Bernard Boullart, Luc
Copyright Year	2008
Abstract	The mathematical representation of human preferences has been a subject of study for researchers in different fields. In multi-criteria decision making (MCDM) and fuzzy modeling, preference models are typically constructed by interacting with the human decision maker (DM). However, it is known that a DM often has difficulties to specify precise values for certain parameters of the model. He/she instead feels more comfortable to give holistic judgements for some of the alternatives. Inference and elicitation procedures then assist the DM to find a satisfactory model and to assess unjudged alternatives. In a related but more statistical way, machine learning algorithms can also infer preference models with similar setups and purposes, but here less interaction with the DM is required/allowed. In this article we discuss the main differences between both types of inference and, in particular, we present a hybrid approach that combines the best of both worlds. This approach consists of a very general kernel-based framework for constructing and inferring preference models. Additive models, for which interpretability is preserved, and utility models can be considered as special cases. Besides generality, important benefits of this approach are its robustness to noise and good scalability. We show in detail how this framework can be utilized to aggregate single-criterion outranking relations, resulting in a flexible class of preference models for which domain knowledge can be specified by a DM.
Starting Page	169
Ending Page	189
Page Count	21
File Format	PDF
ISSN	16194500
Journal	4OR
Volume Number	7
Issue Number	2
e-ISSN	16142411
Language	English
Publisher	Springer-Verlag
Publisher Date	2008-09-18
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Preference relations Kernel methods Aggregation of criteria Inference procedures Quadratic programming Preference learning Industrial and Production Engineering Optimization Operations Research/Decision Theory
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Management Science and Operations Research Management Information Systems Computational Theory and Mathematics

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