3 citations · 10 across the 21 of their papers we have counts for
5 papers · 1 filter
Evolving Features vs Evolving Entire Trees with GP for Interpretable Survival Analysis
Thalea Schlender, Peter A. N. Bosman, Tanja Alderliesten
Survival analysis concerns the task of predicting the time until an event occurs. Often used in the medical field, survival analysis deals with incomplete (i.e., censored) data, fo…
Parallel Adaptive Multi-Objective Evolutionary Learning of Discretized Bayesian Network Classifiers for Clinical Data
Damy M. F. Ha, Thalea Schlender, Yvette M. van der Linden +2
Bayesian Networks (BNs) are of interest from an explainable AI viewpoint, offering transparent probabilistic models for decision support. Baymex is a recently introduced multi-obje…
Sharing Knowledge without Sharing Data: Stitches can improve ensembles of disjointly trained models
Arthur Guijt, Dirk Thierens, Ellen Kerkhof +3
Deep learning has been shown to be very capable at performing many real-world tasks. However, this performance is often dependent on the presence of large and varied datasets. In s…
Iterated Population Based Training with Task-Agnostic Restarts
Alexander Chebykin, Tanja Alderliesten, Peter A. N. Bosman
Hyperparameter Optimization (HPO) can lift the burden of tuning hyperparameters (HPs) of neural networks. HPO algorithms from the Population Based Training (PBT) family are efficie…
Learning Discretized Bayesian Networks with GOMEA
Damy M. F. Ha, Tanja Alderliesten, Peter A. N. Bosman
Bayesian networks model relationships between random variables under uncertainty and can be used to predict the likelihood of events and outcomes while incorporating observed evide…