Fraud Detection On Baf Base

Metrics

Recall @ 1% FPR

Results

Performance results of various models on this benchmark

		Paper Title
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