ECSEL: Explainable Classification via Signomial Equation Learning 事件

ECSEL: Explainable Classification via Signomial Equation Learning arXiv:2601.21789v2 Announce Type: replace-cross Abstract: We introduce ECSEL, an explainable classification method that learns formal expressions in the form of signomial equations, motivated by the observation that many symbolic regression benchmarks admit compact signomial structure. ECSEL directly constructs a structural, closed-form expression that serves as both a classifier and an explanation. On standard symbolic regressio