MARS: Magnitude-Aware Rank Statistics
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Computer Science > Machine Learning
Title:MARS: Magnitude-Aware Rank Statistics
Abstract:Comprehensive evaluation of machine learning models is the key to make sure that they perform as robustly and consistently as desired. In order to summarize the experimental results and pick a winner, Critical Difference (CD) diagrams are used. Standard CD diagrams rely on discrete ranks, discarding the magnitude of performance gaps between models, raising an issue which we call magnitude-blindness. In order to address this issue, we propose Magnitude-Aware Rank Statistics (MARS) that incorporates a relative margin coefficient as a weight for the discrete ranks. This coefficient scales ranks based on the distance between the best and worst performers, with a dynamic projection to handle boundary cases. Followed by the calculation of a CD value, MARS results in a more realistic statistical representation of differences of model performances and more insights on how methods actually perform in vast and extensive experimental settings.
| Comments: | Preprint submitted to Elsevier Pattern Recognition Letters |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.23563 [cs.LG] |
| (or arXiv:2605.23563v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.23563
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Muhammad Rajabinasab [view email][v1] Fri, 22 May 2026 12:29:53 UTC (85 KB)
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