Search Results for author: Bastian Pfeifer

Found 7 papers, 5 papers with code

Federated unsupervised random forest for privacy-preserving patient stratification

no code implementations29 Jan 2024 Bastian Pfeifer, Christel Sirocchi, Marcus D. Bloice, Markus Kreuzthaler, Martin Urschler

In the realm of precision medicine, effective patient stratification and disease subtyping demand innovative methodologies tailored for multi-omics data.

Clustering Feature Importance +1

Be Careful When Evaluating Explanations Regarding Ground Truth

1 code implementation8 Nov 2023 Hubert Baniecki, Maciej Chrabaszcz, Andreas Holzinger, Bastian Pfeifer, Anna Saranti, Przemyslaw Biecek

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.

Explaining and visualizing black-box models through counterfactual paths

1 code implementation15 Jul 2023 Bastian Pfeifer, Mateusz Krzyzinski, Hubert Baniecki, Anna Saranti, Andreas Holzinger, Przemyslaw Biecek

Explainable AI (XAI) is an increasingly important area of machine learning research, which aims to make black-box models transparent and interpretable.

counterfactual Explainable Artificial Intelligence (XAI) +2

Bayesian post-hoc regularization of random forests

1 code implementation6 Jun 2023 Bastian Pfeifer

Random Forests are powerful ensemble learning algorithms widely used in various machine learning tasks.

Ensemble Learning

Parea: multi-view ensemble clustering for cancer subtype discovery

1 code implementation30 Sep 2022 Bastian Pfeifer, Marcus D. Bloice, Michael G. Schimek

We apply and validate our methodology on real-world multi-view cancer patient data.

Clustering

Secondary Use of Clinical Problem List Entries for Neural Network-Based Disease Code Assignment

no code implementations27 Dec 2021 Markus Kreuzthaler, Bastian Pfeifer, Diether Kramer, Stefan Schulz

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.

Language Modelling

Graph-guided random forest for gene set selection

1 code implementation26 Aug 2021 Bastian Pfeifer, Hubert Baniecki, Anna Saranti, Przemyslaw Biecek, Andreas Holzinger

To demonstrate a concrete application example, we focus on bioinformatics, systems biology and particularly biomedicine, but the presented methodology is applicable in many other domains as well.

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