3 citations · 7 across the 10 of their papers we have counts for
6 papers · 1 filter
Evidential Rule Learning for Interpretable Classification with Abstention
Javier Fumanal-Idocin, Javier Andreu-Perez
Interpretable classification often requires more than accurate predictions for real-life deployment: models should be transparent about the evidence behind their decisions and abst…
Interpretable Fuzzy Rule-Based Regression Extension for Ex-Fuzzy Library
Cayan Deniz Kucuktopana, Javier Fumanal-Idocin, Richard Pitts +1
Machine learning models achieve high predictive accuracy in regression tasks, but their deployment in safety-critical and regulated domains requires interpretability. While fuzzy r…
Assessing Reliability of Symbol Detection in Concept Bottleneck Models
Javier Fumanal-Idocin, Javier Andreu-Perez
Concept Bottleneck Models (CBMs) are a relevant tool for explainable Artificial Intelligence because they make their predictions through human-interpretable symbols. However, high…
A Fast Interpretable Fuzzy Tree Learner
Javier Fumanal-Idocin, Raquel Fernandez-Peralta, Javier Andreu-Perez
Fuzzy rule-based systems have been mostly used in interpretable decision-making because of their interpretable linguistic rules. However, interpretability requires both sensible li…
Model-Agnostic Dynamic Feature Selection with Uncertainty Quantification
Javier Fumanal-Idocin, Raquel Fernandez-Peralta, Javier Andreu-Perez
Dynamic feature selection (DFS) addresses budget constraints in decision-making by sequentially acquiring features for each instance, making it appealing for resource-limited scena…
Compact Rule-Based Classifier Learning via Gradient Descent
Javier Fumanal-Idocin, Raquel Fernandez-Peralta, Javier Andreu-Perez
Rule-based models are essential for high-stakes decision-making due to their transparency and interpretability, but their discrete nature creates challenges for optimization and sc…