4 papers
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…
PISA: An AI Pipeline for Interpretable-by-design Survival Analysis Providing Multiple Complexity-Accuracy Trade-off Models
Thalea Schlender, Catharina J. A. Romme, Yvette M. van der Linden +3
Survival analysis is central to clinical research, informing patient prognoses, guiding treatment decisions, and optimising resource allocation. Accurate time-to-event predictions…
A Step towards Interpretable Multimodal AI Models with MultiFIX
Mafalda Malafaia, Thalea Schlender, Tanja Alderliesten +1
Real-world problems are often dependent on multiple data modalities, making multimodal fusion essential for leveraging diverse information sources. In high-stakes domains, such as…