Probabilistic Preferences Composition in the Classification of Apparel Retail Stores

Probabilistic Preferences Composition in the Classification of Apparel Retail Stores

Rodrigo Otávio de Araújo Ribeiro (IBOPE-DTM, Rio de Janeiro, Brazil), Lidia Angulo Meza (Universidade Federal Fluminense, Niteroi, Brazil) and Annibal Parracho Sant'Anna (Universidade Federal Fluminense, Niteroi, Brazil)
Copyright: © 2015 |Pages: 15
DOI: 10.4018/IJBAN.2015100104
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Abstract

This paper employs the probabilistic composition of preferences to classify stores by their operational efficiency. Probabilistic composition of preferences is a multicriteria analysis methodology based on the transformation of assessments by multiple attributes into probabilities of choice. The numerical initial measurements provide estimates for location parameters of probability distributions that are compared to measure the preferences. The probabilities of choice according to each attribute separately are aggregated according to probabilistic composition rules. A classification of two sets of stores into five classes is performed.
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Probabilistic Preferences Composition

The preference for an option is essentially the probability of choosing such option. However, often only attributes of the options are known, or only a ranking of the options on the basis of each of such attributes can be objectively established (Sant’Anna, 2007). The technique of probabilistic composition of preferences extracts from the vector of evaluations of the options according to each attribute a vector of probabilities of choice and combines these particular probabilities of choice into a probability of final choice.

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