11 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…
Hypernetworks for Dynamic Feature Selection
Javier Fumanal-Idocin, Raquel Fernandez-Peralta, Javier Andreu-Perez
Dynamic feature selection (DFS) is a machine learning framework in which features are acquired sequentially for individual samples under budget constraints. The exponential growth…
A Neuro-Symbolic System for Interpretable Multimodal Physiological Signals Integration in Human Fatigue Detection
Mohammadreza Jamalifard, Yaxiong Lei, Parasto Azizinezhad +2
We propose a neuro-symbolic architecture that learns four interpretable physiological concepts, oculomotor dynamics, gaze stability, prefrontal hemodynamics, and multimodal, from e…
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…