Rank Reversal in MCDM Models: Contribution of the Normalization

Irik Z. Mukhametzyanov
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Irik Z. Mukhametzyanov: Ufa State Petroleum Technological University

Chapter Chapter 6 in Normalization of Multidimensional Data for Multi-Criteria Decision Making Problems, 2023, pp 111-127 from Springer

Abstract: Abstract Despite applying the same transformation, the values of each feature are transformed independently during multivariate normalization. In particular, for linear normalization methods for each feature, the compression and shift coefficients are individual and are determined by a set of feature values of different alternatives, “measured” in the scale of this feature. As a result, it is possible to shift the domains of the normalized values of various features relative to each other and different data compression. This entails the priority of the contribution of individual criteria in the performance indicator of the alternatives and is one of the significant reasons for the variation in the ranking results depending on the normalization method. This chapter concentrates on an analysis of the reasons why the ordering of alternatives is different for different normalization methods. Attention is focused on the analysis of various multivariate normalization procedures and the contribution of individual attributes to the performance of alternatives. Several indicators are proposed that determine the priority of the contribution of individual features to the performance indicator of the alternatives.

Keywords: Multivariate normalization; Rank reversal due to normalization; Relative preference for different normalizations (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-031-33837-3_6

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