Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Bommert, Andrea"'
Publikováno v:
Statist. Surv. 18, 163 - 298, 2024
Quantifying the similarity between datasets has widespread applications in statistics and machine learning. The performance of a predictive model on novel datasets, referred to as generalizability, depends on how similar the training and evaluation d
Externí odkaz:
http://arxiv.org/abs/2312.04078
Autor:
Stolte, Marieke, Schreck, Nicholas, Slynko, Alla, Saadati, Maral, Benner, Axel, Rahnenführer, Jörg, Bommert, Andrea
Publikováno v:
PLOS ONE (2024)
Simulation is a crucial tool for the evaluation and comparison of statistical methods. How to design fair and neutral simulation studies is therefore of great interest for researchers developing new methods and practitioners confronted with the choic
Externí odkaz:
http://arxiv.org/abs/2312.04077
Fitting models with high predictive accuracy that include all relevant but no irrelevant or redundant features is a challenging task on data sets with similar (e.g. highly correlated) features. We propose the approach of tuning the hyperparameters of
Externí odkaz:
http://arxiv.org/abs/2106.08105
Autor:
Bommert, Andrea, Rahnenführer, Jörg
For data sets with similar features, for example highly correlated features, most existing stability measures behave in an undesired way: They consider features that are almost identical but have different identifiers as different features. Existing
Externí odkaz:
http://arxiv.org/abs/2009.12075
Autor:
Sun, Xudong, Bommert, Andrea, Pfisterer, Florian, Rahnenführer, Jörg, Lang, Michel, Bischl, Bernd
A novel machine learning optimization process coined Restrictive Federated Model Selection (RFMS) is proposed under the scenario, for example, when data from healthcare units can not leave the site it is situated on and it is forbidden to carry out t
Externí odkaz:
http://arxiv.org/abs/1902.08999
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
In Computational Statistics and Data Analysis March 2020 143
Benchmark of filter methods for feature selection in high-dimensional gene expression survival data.
Autor:
Bommert, Andrea1 (AUTHOR) bommert@statistik.tu-dortmund.de, Welchowski, Thomas2 (AUTHOR), Schmid, Matthias2 (AUTHOR), Rahnenführer, Jörg1 (AUTHOR)
Publikováno v:
Briefings in Bioinformatics. Jan2022, Vol. 23 Issue 1, p1-13. 13p.
Autor:
Bommert, Andrea Martina
In this thesis, four aspects connected to feature selection are analyzed: Firstly, a benchmark of filter methods for feature selection is conducted. Secondly, measures for the assessment of feature selection stability are compared both theoretically
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9928adfd54658fafacee7f38d37689a5
Publikováno v:
Computational & Mathematical Methods in Medicine. 8/1/2017, p1-18. 18p.