Summary
In this paper we explore the possibility of deriving consensus rankings by solving consensus optimization problems, characterizing consensus rankings as suitable complete order relations minimizing the average Kemeny-Snell distance to the individual rankings. This optimization problem can be expressed as a binary programming (BP) problem which can typically be solved reasonably efficiently. The underlying theory is discussed in Sect. 1. Applications of the proposed method given in Sect. 2 include a comparison to other mathematical programming (MP) approaches using the data set of Tse [9] and establishing a consensus ranking of marketing journals identified by domain experts from a subset of the Harzing journal quality list [2]. In Sect. 3 we discuss computational details and present the results of a benchmark experiment comparing the performance of the commercial solver CPLEX to three open source mixed integer linear programming (MILP) solvers
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Stecking, R., Schebesch, K.B. (2009). Data Similarity in Classification and Fictitious Training Data Generation. In: Fleischmann, B., Borgwardt, KH., Klein, R., Tuma, A. (eds) Operations Research Proceedings 2008., vol 2008. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00142-0_64
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DOI: https://doi.org/10.1007/978-3-642-00142-0_64
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