KANs applied to GMSC credit data produce a small AUC gain over borrowed baseline numbers, with interpretability shown only through the model's own attribution scores.
Personal credit default prediction fusion frame- work based on self-attention and cross-network algorithms
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
q-fin.RM 1years
2024 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
KACDP: A Highly Interpretable Credit Default Prediction Model
KANs applied to GMSC credit data produce a small AUC gain over borrowed baseline numbers, with interpretability shown only through the model's own attribution scores.