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| 001 | 1010405 | ||
| 005 | 20230905204623.0 | ||
| 024 | 7 | _ | |a 10.34734/FZJ-2023-03045 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2023-03045 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Hamdan, Sami |0 P:(DE-Juel1)184874 |b 0 |e Corresponding author |
| 111 | 2 | _ | |a Organization for Human Brain Mapping (OHBM) |c Montreal |d 2023-07-22 - 2023-07-26 |w Canada |
| 245 | _ | _ | |a Cofound-Leakage: Confound Removal In Machine Learning Leads To Leakage |
| 260 | _ | _ | |c 2023 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
| 336 | 7 | _ | |a conferenceObject |2 DRIVER |
| 336 | 7 | _ | |a CONFERENCE_POSTER |2 ORCID |
| 336 | 7 | _ | |a Output Types/Conference Poster |2 DataCite |
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| 500 | _ | _ | |a Acknowledgments: This work was partly supported by the Helmholtz-AI project DeGen, the Helmholtz Portfolio Theme ‘Supercomputing and Modeling for the Human Brain’ and Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Poster: Pitfalls of Confound Regression in Machine Learning. Der Postertitel lautet anders, doch das war ok fuer OHBM Veranstalter |
| 520 | _ | _ | |a Modern Machine Learning (ML) approaches are now regularly employed forindividual-level prediction, e.g. personalized medicine.Particularly in such critical-decision making, it is of utmost importance to not onlyachieve high accuracy but also to trust that models rely on actual features-targetrelationships [1, 2]. To this end, it is crucial to consider confounding variables as theycan obstruct the features-target relationship. For instance, a researcher might wantto identify a biomarker showing high classification accuracy between controls andpatients. However, the model might have just learned simpler confounders like ageor sex as a good proxy of the disease [3]. To counteract such unwanted confoundingeffects, investigators often use linear models to remove confounding variables fromeach feature separately before employing ML. While this confound regression (CR)approach is popular [4], its pitfalls, especially when paired with non-linear MLmodels, are not well understood. |
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| 700 | 1 | _ | |a Love, Bradley C. |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Polier, Georg von |0 P:(DE-HGF)0 |b 2 |
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| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1010405/files/SamiHamdanOHBM.pdf |y OpenAccess |
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| 914 | 1 | _ | |y 2023 |
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