3 citations · 3 across the 3 of their papers we have counts for
3 papers · 1 filter
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…
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…
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…