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Learning monotone preferences using a majority rule sorting model
Author(s) -
Sobrie Olivier,
Mousseau Vincent,
Pirlot Marc
Publication year - 2019
Publication title -
international transactions in operational research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.032
H-Index - 52
eISSN - 1475-3995
pISSN - 0969-6016
DOI - 10.1111/itor.12512
Subject(s) - sort , monotone polygon , sorting , basis (linear algebra) , electre , mathematics , function (biology) , mathematical optimization , object (grammar) , computer science , artificial intelligence , multiple criteria decision analysis , algorithm , arithmetic , evolutionary biology , biology , geometry
Abstract We consider the problem of learning a function assigning objects into ordered categories. The objects are described by a vector of attribute values and the assignment function is monotone w.r.t. the attribute values (monotone sorting problem). Our approach is based on a model used in multicriteria decision analysis (MCDA), called MR‐Sort. This model determines the assigned class on the basis of a majority rule and an artificial object that is a typical lower profile of the category. MR‐Sort is a simplified variant of the ELECTRE TRI method. We describe an algorithm designed for learning such a model on the basis of assignment examples. We compare its performance with choquistic regression, a method recently proposed in the preference learning community, and with UTADIS, another MCDA method leaning on an additive value function (utility) model. Our experimentation shows that MR‐Sort competes with the other two methods, and leads to a model that is interpretable.