activity
20202026
most citedPISA: An AI Pipeline for Interpretable-by-design Survival Analysis Providing Multiple Complexity-Accuracy Trade-off Models

1 citations · 1 across the 5 of their papers we have counts for

collaborators

7 papers

cs.LG2026

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…

cs.LG2026

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…

stat.AP2025★ 1 cited

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…

cs.NE2025

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…

cs.AI2024

MultiFIX: An XAI-friendly feature inducing approach to building models from multimodal data

Mafalda Malafaia, Thalea Schlender, Peter A. N. Bosman +1

In the health domain, decisions are often based on different data modalities. Thus, when creating prediction models, multimodal fusion approaches that can extract and combine relev…

cs.NE2024

Improving the efficiency of GP-GOMEA for higher-arity operators

Thalea Schlender, Mafalda Malafaia, Tanja Alderliesten +1

Deploying machine learning models into sensitive domains in our society requires these models to be explainable. Genetic Programming (GP) can offer a way to evolve inherently inter…

cs.CL2020

"Thy algorithm shalt not bear false witness": An Evaluation of Multiclass Debiasing Methods on Word Embeddings

Thalea Schlender, Gerasimos Spanakis

With the vast development and employment of artificial intelligence applications, research into the fairness of these algorithms has been increased. Specifically, in the natural la…