3 papers
cs.LG2025
Enhancing interpretability of rule-based classifiers through feature graphs
Christel Sirocchi, Damiano Verda
In domains where transparency and trustworthiness are crucial, such as healthcare, rule-based systems are widely used and often preferred over black-box models for decision support…
cs.LG2024
Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping
Christel Sirocchi, Martin Urschler, Bastian Pfeifer
Interpretable machine learning has emerged as central in leveraging artificial intelligence within high-stakes domains such as healthcare, where understanding the rationale behind…
cs.LG2024
Federated unsupervised random forest for privacy-preserving patient stratification
Bastian Pfeifer, Christel Sirocchi, Marcus D. Bloice +2
In the realm of precision medicine, effective patient stratification and disease subtyping demand innovative methodologies tailored for multi-omics data. Clustering techniques appl…