Validation-Stage Combinatorial Fusion Analysis for Imbalanced Credit-Card Fraud Detection
Credit-card fraud detection is difficult because fraudulent transactions are rare, costly, and unevenly distributed. Strong gradient-boosted tree models already perform well on structured transaction data, so the value of another fusion method is not obvious. This paper examines whether Combinatorial Fusion Analysis (CFA), which searches over model subsets and rank-score fusion rules, can still add value on the IEEE-CIS Fraud Detection benchmark. Using a leakage-free 60/20/20 train/validation/test protocol, we evaluate 480 fusion configurations built from seven base classifiers. The best test-set result comes from diversity-weighted score fusion of Random Forest, XGBoost, and LightGBM (DE...
| Authors | Xiao Han, Chenyu Wu |
|---|---|
| Primary category | cs.LG |
| Published | 9 June 2026 |
| Last updated at source | 9 June 2026 |
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