Evidence map›Paper›PMID 42686763›Full record

ArticleScientific reports2026

Improving Fairness in Doubly Imbalanced Datasets.

Ata Yalcin, Asli Umay Ozturk, Yigit Sever, Viktoria Pauw, Stephan Hachinger, Ismail Hakki Toroslu, Pinar Karagoz

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PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Ata YalcinDepartment of Computer Engineering, Middle East Technical University (METU), Ankara, Turkey.
Asli Umay OzturkDepartment of Computer Engineering, Middle East Technical University (METU), Ankara, Turkey.
Yigit SeverDepartment of Computer Engineering, Middle East Technical University (METU), Ankara, Turkey.
Viktoria PauwLeibniz Supercomputing Centre (LRZ), Garching, Germany.
Stephan HachingerLeibniz Supercomputing Centre (LRZ), Garching, Germany.
Ismail Hakki TorosluDepartment of Computer Engineering, Middle East Technical University (METU), Ankara, Turkey.
Pinar KaragozDepartment of Computer Engineering, Middle East Technical University (METU), Ankara, Turkey. karagoz@ceng.metu.edu.tr.

Funding

EXA4MIND project, funded by the European Union's Horizon Europe Research and Innovation Programme 101092944
6 · The paper itself

Abstract

Fairness has been identified as an important aspect of Machine Learning and Artificial Intelligence solutions for decision making. Recent literature offers a variety of approaches for debiasing, however many of them fall short when the data collection is imbalanced. In this paper, we focus on a particular case, fairness in doubly imbalanced datasets, such that the data collection is imbalanced both for the label and the groups in the sensitive attribute. Firstly, we present an exploratory analysis to illustrate limitations in debiasing on a doubly imbalanced dataset. Then, a multi-criteria based solution is proposed for finding the most suitable sampling and distribution for the label and the sensitive attribute, in terms of fairness and classification accuracy.

Indexed as

Algorithmic fairnessFair AIFraud detectionImbalanced dataMulti-parameter optimization

Identifiers

PMID42686763
PMCPMC13538691

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.