Author

Javier Fumanal-Idocin

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#machine learning May 2026

Hypernetworks for Dynamic Feature Selection

This paper proposes a hypernetwork-based DFS approach that generates feature subset-specific classifier parameters on demand, and shows stronger zero-shot generalisation to feature subsets never seen during training than existing DFS approaches.

Javier Fumanal-Idocin, R. Fernandez-Peralta, Javier Andréu-Pérez · arXiv.org · 0 citations

Preprint Aug 2026

Evidential Rule Learning for Interpretable Classification with Abstention

Fast Evidential Rule Learning (FERL) is introduced, a method that learns interpretable, accurate fuzzy rule models whose outputs are evidential, and is Lipschitz stable, which means that its evidential outputs vary smoothly with the input.

Javier Fumanal-Idocin, Javier Andreu-Perez · 0 citations