Unsupervised Learning of True Ranking Estimators using the Belief Function Framework

Argentini, Andrea and Blanzieri, Enrico (2011) Unsupervised Learning of True Ranking Estimators using the Belief Function Framework. UNSPECIFIED.

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    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.

    Item Type: Departmental Technical Report
    Department or Research center: Information Engineering and Computer Science
    Subjects: Q Science > QA Mathematics > QA076 Computer software
    Uncontrolled Keywords: Ranking Aggregation, Belief Function, Dempster-Shafer Theory
    Additional Information: This work has been presented at 11th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU) as poster.
    Report Number: DISI-11-480
    Repository staff approval on: 02 Nov 2011

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