3 citations · 7 across the 10 of their papers we have counts for
5 papers · 1 filter
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…