https://github.com/tigerchen52/pub_guard_llm
공식 코드Pub-Guard-LLM: Detecting Fraudulent Biomedical Articles with Reliable Explanations
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Fraud Detection is a vital topic that applies to many industries including the financial sectors, banking, government agencies, insurance, and law enforcement, and more. Fraud endeavors have detected a radical rise in current years, creating this topic more critical than ever. Despite struggles on the part of the troubled organizations, hundreds of millions of dollars are wasted to fraud each year. Because nearly a few samples confirm fraud in a vast community, locating these can be complex. Data mining and statistics help to predict and immediately distinguish fraud and take immediate action to minimize costs.
Source: Applying support vector data description for fraud detection
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Pub-Guard-LLM: Detecting Fraudulent Biomedical Articles with Reliable Explanations
Locally Interpretable One-Class Anomaly Detection for Credit Card Fraud Detection
AD-AGENT: A Multi-agent Framework for End-to-end Anomaly Detection
AD-LLM: Benchmarking Large Language Models for Anomaly Detection
Text generation for dataset augmentation in security classification tasks
Towards Reliable Rare Category Analysis on Graphs via Individual Calibration
Cost Sensitive GNN-based Imbalanced Learning for Mobile Social Network Fraud Detection
GAT-COBO: Cost-Sensitive Graph Neural Network for Telecom Fraud Detection
EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection
PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection
TOD: GPU-accelerated Outlier Detection via Tensor Operations
Multiple perspectives HMM-based feature engineering for credit card fraud detection
SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training
SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training
Credit Card Fraud Detection Using Autoencoder Neural Network
Bayesian Stress Testing of Models in a Classification Hierarchy
VOS: a Method for Variational Oversampling of Imbalanced Data
Credit Card Fraud Detection Using Autoencoder Neural Network
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.