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Fraud Detection On Baf Base

Metrics

Recall @ 1% FPR

Results

Performance results of various models on this benchmark

Paper Title
LightGBM25.2%RIFF: Inducing Rules for Fraud Detection from Decision Trees
FIGS21%RIFF: Inducing Rules for Fraud Detection from Decision Trees
CART+RIFF18.4%RIFF: Inducing Rules for Fraud Detection from Decision Trees
CART16%RIFF: Inducing Rules for Fraud Detection from Decision Trees
FIGS+RIFF15.8%RIFF: Inducing Rules for Fraud Detection from Decision Trees
FIGU+RIFF15.5%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