Integrating Expert Knowledge into Kernel-based Preference Models
Willem Waegeman, Bernard De Baets, Luc Boullart · Ghent University Academic Bibliography (Ghent University) · 2008
In multi-criteria decision making (MCDM) and fuzzy modeling, preference models are typically constructed by interacting with the human decision maker (DM). When the DM experiences difficulties to specify precise for all parameters of the model, inference and elicitation procedures can assist him/her to find a satisfactory model and to assess unlabelled examples. 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 integrated. We present a hybrid approach that combines the best of both worlds. It consists of a general kernel-based framework for constructing and inferring preference models, in which expert knowledge can be included. 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.