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Fraud Detection On Baf Base

Metrics

Recall @ 1% FPR

Results

Performance results of various models on this benchmark

		Paper Title	Repository
LightGBM	25.2%	RIFF: Inducing Rules for Fraud Detection from Decision Trees	-
FIGS	21%	RIFF: Inducing Rules for Fraud Detection from Decision Trees	-
CART+RIFF	18.4%	RIFF: Inducing Rules for Fraud Detection from Decision Trees	-
CART	16%	RIFF: Inducing Rules for Fraud Detection from Decision Trees	-
FIGS+RIFF	15.8%	RIFF: Inducing Rules for Fraud Detection from Decision Trees	-
FIGU+RIFF	15.5%	RIFF: Inducing Rules for Fraud Detection from Decision Trees	-
LightGBM	-	Exploring Neural Joint Activity in Spiking Neural Networks for Fraud Detection	-
1D-CSNN	-	Exploring Neural Joint Activity in Spiking Neural Networks for Fraud Detection	-
MLP–NN	-	Decoupling Decision-Making in Fraud Prevention through Classifier Calibration for Business Logic Action	-
CatBoost	-	Decoupling Decision-Making in Fraud Prevention through Classifier Calibration for Business Logic Action	-
1D-CSNN	-	Improving Fraud Detection with 1D-Convolutional Spiking Neural Networks Through Bayesian Optimization	-
LightGBM	-	Decoupling Decision-Making in Fraud Prevention through Classifier Calibration for Business Logic Action	-

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Fraud Detection On Baf Base | SOTA | HyperAI