3 citations · 7 across the 10 of their papers we have counts for
10 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…
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…
Crisp complexity of fuzzy classifiers
Raquel Fernandez-Peralta, Javier Fumanal-Idocin, Javier Andreu-Perez
Rule-based systems are a very popular form of explainable AI, particularly in the fuzzy community, where fuzzy rules are widely used for control and classification problems. Howeve…
Reliable Classification with Conformal Learning and Interval-Type 2 Fuzzy Sets
Javier Fumanal-Idocin, Javier Andreu-Perez
Classical machine learning classifiers tend to be overconfident can be unreliable outside of the laboratory benchmarks. Properly assessing the reliability of the output of the mode…
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…
ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniques
Javier Fumanal-Idocin, Javier Andreu-Perez, Oscar Cordón +2
Automatic art analysis employs different image processing techniques to classify and categorize works of art. When working with artistic images, we need to take into account furthe…