A variant of the ranking aggregation problem is considered in this work. The goal is to find an approximation of an unknown true ranking given a set of rankings. We devise a solution called Belief Ranking Estimator (BRE), based on the belief function framework that permits to represent beliefs on the correctness of the rankings position as well as uncertainty on the quality of the rankings from the subjective point of view of the expert. The results of a preliminary empirical comparison of BRE against baseline ranking estimators and state-of-the-art methods for ranking aggregation are shown and discussed.

Unsupervised Learning of True Ranking Estimators using the Belief Function Framework / Blanzieri, Enrico; Argentini, Andrea. - ELETTRONICO. - (2011), pp. 1-10.

Unsupervised Learning of True Ranking Estimators using the Belief Function Framework

Blanzieri, Enrico
Ultimo
;
Argentini, Andrea
Primo
2011-01-01

Abstract

A variant of the ranking aggregation problem is considered in this work. The goal is to find an approximation of an unknown true ranking given a set of rankings. We devise a solution called Belief Ranking Estimator (BRE), based on the belief function framework that permits to represent beliefs on the correctness of the rankings position as well as uncertainty on the quality of the rankings from the subjective point of view of the expert. The results of a preliminary empirical comparison of BRE against baseline ranking estimators and state-of-the-art methods for ranking aggregation are shown and discussed.
2011
Trento
Università degli Studi di Trento, Dipartimento di Ingegneria e Scienza dell'Informazione
Unsupervised Learning of True Ranking Estimators using the Belief Function Framework / Blanzieri, Enrico; Argentini, Andrea. - ELETTRONICO. - (2011), pp. 1-10.
Blanzieri, Enrico; Argentini, Andrea
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/359691
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