https://github.com/4paradigm/featinsight
공식 코드FeatInsight: An Online ML Feature Management System on 4Paradigm Sage-Studio Platform
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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.
FeatInsight: An Online ML Feature Management System on 4Paradigm Sage-Studio Platform
Local Subspace-Based Outlier Detection using Global Neighbourhoods
DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network Data
The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth Measure
The Area of the Convex Hull of Sampled Curves: a Robust Functional Statistical Depth Measure
A Variational Approach for Learning from Positive and Unlabeled Data
Spotting Collective Behaviour of Online Frauds in Customer Reviews
Are You for Real? Detecting Identity Fraud via Dialogue Interactions
Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced Datasets
A Framework for Large Scale Synthetic Graph Dataset Generation
TSPP: A Unified Benchmarking Tool for Time-series Forecasting
SeqNAS: Neural Architecture Search for Event Sequence Classification
Detecting organized eCommerce fraud using scalable categorical clustering
robROSE: A robust approach for dealing with imbalanced data in fraud detection
Explaining Arguments' Strength: Unveiling the Role of Attacks and Supports (Technical Report)
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters
Itsy Bitsy SpiderNet: Fully Connected Residual Network for Fraud Detection
Enhancing Fairness in Unsupervised Graph Anomaly Detection through Disentanglement
Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation
Dynamic Relation-Attentive Graph Neural Networks for Fraud Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.