DistilBERT reaches AUC 1.0000 and F1 0.9981 for BEC detection while CatBoost reaches AUC 0.9860 and F1 0.9382, with a three-way cost-sensitive policy optimizing under a 1:5167 false-negative to false-positive ratio.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Semantic Superiority vs. Forensic Efficiency: A Comparative Analysis of Deep Learning and Psycholinguistics for Business Email Compromise Detection
DistilBERT reaches AUC 1.0000 and F1 0.9981 for BEC detection while CatBoost reaches AUC 0.9860 and F1 0.9382, with a three-way cost-sensitive policy optimizing under a 1:5167 false-negative to false-positive ratio.