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Kernel-based learning methods for preference aggregation

Willem Waegeman (UGent) , Bernard De Baets (UGent) and Luc Boullart (UGent)
(2009) 4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH. 7(2). p.169-189
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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.
Keywords
Preference relations, Inference procedures, Aggregation of criteria, Kernel methods, Quadratic programming, Preference learning, ORDINAL REGRESSION, ASSIGNMENT EXAMPLES, ROC ANALYSIS, FUZZY, ALTERNATIVES, INFORMATION, RANKING, CONSTRUCTION, TRANSITIVITY, WEIGHTS

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MLA
Waegeman, Willem, et al. “Kernel-Based Learning Methods for Preference Aggregation.” 4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH, vol. 7, no. 2, 2009, pp. 169–89, doi:10.1007/s10288-008-0085-5.
APA
Waegeman, W., De Baets, B., & Boullart, L. (2009). Kernel-based learning methods for preference aggregation. 4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH, 7(2), 169–189. https://doi.org/10.1007/s10288-008-0085-5
Chicago author-date
Waegeman, Willem, Bernard De Baets, and Luc Boullart. 2009. “Kernel-Based Learning Methods for Preference Aggregation.” 4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH 7 (2): 169–89. https://doi.org/10.1007/s10288-008-0085-5.
Chicago author-date (all authors)
Waegeman, Willem, Bernard De Baets, and Luc Boullart. 2009. “Kernel-Based Learning Methods for Preference Aggregation.” 4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH 7 (2): 169–189. doi:10.1007/s10288-008-0085-5.
Vancouver
1.
Waegeman W, De Baets B, Boullart L. Kernel-based learning methods for preference aggregation. 4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH. 2009;7(2):169–89.
IEEE
[1]
W. Waegeman, B. De Baets, and L. Boullart, “Kernel-based learning methods for preference aggregation,” 4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH, vol. 7, no. 2, pp. 169–189, 2009.
@article{721403,
  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.}},
  author       = {{Waegeman, Willem and De Baets, Bernard and Boullart, Luc}},
  issn         = {{1619-4500}},
  journal      = {{4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH}},
  keywords     = {{Preference relations,Inference procedures,Aggregation of criteria,Kernel methods,Quadratic programming,Preference learning,ORDINAL REGRESSION,ASSIGNMENT EXAMPLES,ROC ANALYSIS,FUZZY,ALTERNATIVES,INFORMATION,RANKING,CONSTRUCTION,TRANSITIVITY,WEIGHTS}},
  language     = {{eng}},
  number       = {{2}},
  pages        = {{169--189}},
  title        = {{Kernel-based learning methods for preference aggregation}},
  url          = {{http://doi.org/10.1007/s10288-008-0085-5}},
  volume       = {{7}},
  year         = {{2009}},
}
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