9 papers
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…
Interpreting Contrastive Embeddings in Specific Domains with Fuzzy Rules
Javier Fumanal-Idocin, Mohammadreza Jamalifard, Javier Andreu-Perez
Free-style text is still one of the common ways in which data is registered in real environments, like legal procedures and medical records. Because of that, there have been signif…
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…
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…