Zobrazeno 1 - 10
of 107
pro vyhledávání: '"PFEIFER, Bastian"'
Interpretable machine learning has emerged as central in leveraging artificial intelligence within high-stakes domains such as healthcare, where understanding the rationale behind model predictions is as critical as achieving high predictive accuracy
Externí odkaz:
http://arxiv.org/abs/2404.17886
Autor:
Pfeifer, Bastian, Sirocchi, Christel, Bloice, Marcus D., Kreuzthaler, Markus, Urschler, Martin
In the realm of precision medicine, effective patient stratification and disease subtyping demand innovative methodologies tailored for multi-omics data. Clustering techniques applied to multi-omics data have become instrumental in identifying distin
Externí odkaz:
http://arxiv.org/abs/2401.16094
Autor:
Baniecki, Hubert, Chrabaszcz, Maciej, Holzinger, Andreas, Pfeifer, Bastian, Saranti, Anna, Biecek, Przemyslaw
Evaluating explanations of image classifiers regarding ground truth, e.g. segmentation masks defined by human perception, primarily evaluates the quality of the models under consideration rather than the explanation methods themselves. Driven by this
Externí odkaz:
http://arxiv.org/abs/2311.04813
Autor:
Pfeifer, Bastian, Krzyzinski, Mateusz, Baniecki, Hubert, Saranti, Anna, Holzinger, Andreas, Biecek, Przemyslaw
Explainable AI (XAI) is an increasingly important area of machine learning research, which aims to make black-box models transparent and interpretable. In this paper, we propose a novel approach to XAI that uses the so-called counterfactual paths gen
Externí odkaz:
http://arxiv.org/abs/2307.07764
Autor:
Pfeifer, Bastian
Random Forests are powerful ensemble learning algorithms widely used in various machine learning tasks. However, they have a tendency to overfit noisy or irrelevant features, which can result in decreased generalization performance. Post-hoc regulari
Externí odkaz:
http://arxiv.org/abs/2306.03702
Multi-view clustering methods are essential for the stratification of patients into sub-groups of similar molecular characteristics. In recent years, a wide range of methods has been developed for this purpose. However, due to the high diversity of c
Externí odkaz:
http://arxiv.org/abs/2209.15399
Publikováno v:
In Information Sciences February 2025 690
Clinical information systems have become large repositories for semi-structured and partly annotated electronic health record data, which have reached a critical mass that makes them interesting for supervised data-driven neural network approaches. W
Externí odkaz:
http://arxiv.org/abs/2112.13756
Publikováno v:
Sci Rep 12, 16857 (2022)
Machine learning methods can detect complex relationships between variables, but usually do not exploit domain knowledge. This is a limitation because in many scientific disciplines, such as systems biology, domain knowledge is available in the form
Externí odkaz:
http://arxiv.org/abs/2108.11674
Autor:
Metsch, Jacqueline Michelle, Saranti, Anna, Angerschmid, Alessa, Pfeifer, Bastian, Klemt, Vanessa, Holzinger, Andreas, Hauschild, Anne-Christin
Publikováno v:
In Journal of Biomedical Informatics February 2024 150