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Ranking with ties based on noisy performance data
RWTH Aachen University, Aachen, Germany.
Umeå University, Faculty of Science and Technology, Department of Computing Science. Umeå University, Faculty of Science and Technology, High Performance Computing Center North (HPC2N).ORCID iD: 0000-0002-4675-7434
Umeå University, Faculty of Science and Technology, Department of Computing Science. Umeå University, Faculty of Science and Technology, High Performance Computing Center North (HPC2N).ORCID iD: 0000-0002-4972-7097
2025 (English)In: International Journal of Data Science and Analytics, ISSN 2364-415XArticle in journal (Refereed) Epub ahead of print
Abstract [en]

We consider the problem of ranking a set of objects based on their performance when the measurement of said performance is subject to noise. In this scenario, the performance is measured repeatedly, resulting in a range of measurements for each object. If the ranges of two objects do not overlap, then we consider one object as ‘better’ than the other, and we expect it to receive a higher rank; if, however, the ranges overlap, then the objects are incomparable, and we wish them to be assigned the same rank. Unfortunately, the incomparability relation of ranges is in general not transitive; as a consequence, in general the two requirements cannot be satisfied simultaneously, i.e., it is not possible to guarantee both distinct ranks for objects with separated ranges, and same rank for objects with overlapping ranges. This conflict leads to more than one reasonable way to rank a set of objects. Although the problem of ranking with ties has been widely studied, there remains a lack of clarity regarding what constitutes a set of reasonable rankings. In this paper, we explore the ambiguities that arise when ranking with ties, and define a set of reasonable rankings, which we call partial rankings. We develop and analyze three different methodologies to compute a partial ranking. Finally, we show how performance differences among objects can be investigated with the help of partial ranking.

Place, publisher, year, edition, pages
Springer, 2025.
Keywords [en]
Knowledge discovery, Noise, Partial orders, Performance, Ranking
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:umu:diva-236240DOI: 10.1007/s41060-025-00722-1ISI: 001411719700001Scopus ID: 2-s2.0-85218821095OAI: oai:DiVA.org:umu-236240DiVA, id: diva2:1948828
Funder
German Research Foundation (DFG), IRTG 2379Available from: 2025-04-01 Created: 2025-04-01 Last updated: 2025-04-01

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Karlsson, LarsBientinesi, Paolo

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