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Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping
Graph-based feature selection methods are extensively evaluated on synthetic and benchmark datasets both in terms of their ability to reduce dimensionality while improving clustering performance, as well as to enhance model interpretability.
Federated unsupervised random forest for privacy-preserving patient stratification
In the realm of precision medicine, effective patient stratification and disease subtyping demand innovative methodologies tailored for multi-omics data.
Distributed averaging for accuracy prediction in networked systems
Distributed averaging is among the most relevant cooperative control problems, with applications in sensor and robotic networks, distributed signal processing, data fusion, and load balancing.
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