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Fraud Detection On Baf Base

Métriques

Recall @ 1% FPR

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Recall @ 1% FPR	Paper Title	Repository
FIGS	21%	RIFF: Inducing Rules for Fraud Detection from Decision Trees	-
CART	16%	RIFF: Inducing Rules for Fraud Detection from Decision Trees	-
LightGBM	-	Exploring Neural Joint Activity in Spiking Neural Networks for Fraud Detection
FIGU+RIFF	15.5%	RIFF: Inducing Rules for Fraud Detection from Decision Trees	-
1D-CSNN	-	Exploring Neural Joint Activity in Spiking Neural Networks for Fraud Detection
CART+RIFF	18.4%	RIFF: Inducing Rules for Fraud Detection from Decision Trees	-
MLP–NN	-	Decoupling Decision-Making in Fraud Prevention through Classifier Calibration for Business Logic Action	-
FIGS+RIFF	15.8%	RIFF: Inducing Rules for Fraud Detection from Decision Trees	-
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	-
LightGBM	25.2%	RIFF: Inducing Rules for Fraud Detection from Decision Trees	-

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