Learning lexicographic orders

Dombi, József; Imreh, Csanád; Vincze, Nándor

doi:10.1016/j.ejor.2006.10.029

Cited by 19 publications

(9 citation statements)

References 9 publications

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“…In all these cases, each piece of collected information then helps to better identify the preference model of the DM. Also, the elicitation processes may differ greatly between the different MCDA models, be it Choquet Integrals [28], GAI-networks [27], CP-net [6], lexicographic orderings [26,22] and their conditional extensions [4], . .…”

Section: Introductionmentioning

confidence: 99%

A generic framework to include belief functions in preference handling and multi-criteria decision

Destercke

2018

International Journal of Approximate Reasoning

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Modelling the preferences of a decision maker about alternatives having multiple criteria usually starts by collecting preference information (comparisons of alternatives, importance of criteria, . . . ), which are then used to fit a preference model issued from some set of hypothesis (weighted average, CP-net, lexicographic orderings, . . . ). In practice, this process may often lead to inconsistencies that may be due to inaccurate information provided by the decision maker, who can be unsure about the provided information, or to a poor choice of hypothesis set, which can be too restrictive or not well adapted to the decision process. In this paper, we propose to use belief functions as a way to quantify and resolve such inconsistencies, notably by allowing the decision maker to express her/his certainty about the provided preferential information. Our framework is generic, in the sense that it does not assume a given set of hypothesis a priori, and is consistent with precise methods, in the sense that in the absence of uncertainty and inconsistencies in the information, precise models are ultimately retrieved.

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Section: Introductionmentioning

confidence: 99%

A generic framework to include belief functions in preference handling and multi-criteria decision

Destercke

2018

International Journal of Approximate Reasoning

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“…In fact, the most studied model up to now is lexicographic preferences for binary attributes. The problem is first addressed in [14], which investigate several problems regarding learning lexicographical preference relations. First they give a polynomial-time algorithm which, from a set of pairwise comparisons between vectors of values, determines the importance order of the criteria if it is possible, or decides whether the sample is insufficient or inconsistent.…”

Section: Introductionmentioning

confidence: 99%

Learning Ordinal Preferences on Multiattribute Domains: The Case of CP-nets

Chevaleyre¹,

Koriche²,

Lang³

et al. 2010

Preference Learning

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A recurrent issue in automated decision making is to extract a preference structure from a set of examples. In this paper, we investigate the problem of learning ordinal preference orderings over discrete multi-attribute, or combinatorial, domains. Specifically, we concentrate on the learnability issue of conditional preference networks, or CP-nets, that have recently emerged as a popular graphical language for representing ordinal preferences in a concise and intuitive manner. This paper provides results in both passive and active learning. In passive learning, the learner aims at finding a CP-net compatible with a given set of examples, while in active learning the learner searches for the cheapest interaction policy with the user for acquiring the target CP-net.

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“…This area is often also referred to as preference elicitation (Haddawy et al 2003;Bonilla et al 2010). An alternative line of research investigates special types of preference models such as outranking relations (Greco et al 2011), rough set-based decision rules (Greco et al 2001), lexicographic orders (Dombi et al 2007;Schmitt and Martignon 2006;Flach and Matsubara 2007;Yaman et al 2011) and CP-nets (Boutilier et al 2004). Popular application areas include collaborative filtering, where a customer's preferences are estimated from the preferences of other customers (Breese et al 1998;Sarwar et al 2001), and personalized ranking techniques for information retrieval (Radlinski and Joachims 2005;Radlinski et al 2011;Rudin 2009).…”

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confidence: 99%