Leveraging preference disaggregation for context-dependent adaptive multi-criteria sorting with incomplete information

In this paper we face the multicriteria sorting problem considering that we have incomplete information.Multicriteria sorting is a particular case of classification problems.It consists in the assignment of some actions to some pre-defined classes.Classification refers to problems where the classes (groups, categories) have been defined in a nominal way.We use the term "incomplete information" to indicate the absence of a value in some criterion of the object to be assigned to a class (category).

Multicriteria sorting using a valued indifference relation under a preference disaggregation paradigm

Lexicographic optimization-based approaches to learning a representative model for multi-criteria sorting with non-monotonic criteria

Multicriteria sorting involves assigning the objects of decisions (actions) into <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$a$ </tex-math></inline-formula> priori known ordered classes considering the preferences of a decision maker (DM). Two new multicriteria sorting methods were recently proposed by the authors. These methods are based on a novel approach called interval-based outranking which provides the methods with attractive practical and theoretical characteristics. However, as is well known, defining parameter values for methods based on the outranking approach is often very difficult. This difficulty arises not only from the large number of parameters and the DM’s lack of familiarity with them, but also from imperfectly known (even missing) information. Here, we address: i) how to elicit the parameter values of the two new methods, and ii) how to incorporate imperfect knowledge during the elicitation. We follow the preference disaggregation paradigm and use evolutionary algorithms to address it. Our proposal performs very well in a wide range of computational experiments. Interesting findings are: i) the method restores the assignment examples with high effectiveness using only three profiles in each limiting boundary or representative actions per class; and ii) the ability to appropriately assign unknown actions can be greatly improved by increasing the number of limiting profiles.