Latest Research in Imbalanced Data Classification Techniques

Top Research Topics in Imbalanced Data Classification Techniques

• Imbalanced Data Classification Techniques — 19 papers
• Explainable Artificial Intelligence (XAI) — 2 papers
• Sound — 1 papers
• Machine Learning — 1 papers
• Financial Distress and Bankruptcy Prediction — 1 papers
• Data Stream Mining Techniques — 1 papers
• Spam and Phishing Detection — 1 papers
• Cryptography and Security — 1 papers

Highest-Cited Papers

1. TeleAntiFraud 2.0: A Refreshable, Profile-Grounded, and Audio-Based Benchmark for Telecom Fraud Detection
2. Anomaly Detection in General Ledger Data: Results from a Hybrid Approach
3. Cloud-Based Distributed Deep Learning for Credit Card Fraud Detection: A Scalability and Data Partitioning Analysis
4. The Effectiveness of Artificial Intelligence In Enhancing Fraud Detection in Financial Systems
5. The Effectiveness of Artificial Intelligence In Enhancing Fraud Detection in Financial Systems
6. Design of a financial fraud detection model optimized by multi-task learning and graph neural networks
7. Machine Learning Applications in Smartphone Healthcare Ser-vices and Fraud Detection: A Ten-Year Systematic Literature Re-view
8. Machine Learning Applications in Smartphone Healthcare Ser-vices and Fraud Detection: A Ten-Year Systematic Literature Re-view
9. Financial fraud detection model based on dual-layer knowledge graph
10. Adversarial Training–Based Deep Imbalanced Learning
11. An explainable detection framework for health insurance fraud via temporal capture and confidence assurance
12. TEMPLAR fraud a verifier grounded calibrated and cost sensitive framework for transaction fraud detection
13. The Explainability–Reliability Gap in Fraud Detection: Evidence from SHAP and Permutation Importance Under Distribution Shift
14. A drift adaptive framework for detecting and tracking evolving anomalies in financial transaction streams
15. Designing Fraud Detection and Recovery Systems: Patterns and Trade-offs
16. Designing Fraud Detection and Recovery Systems: Patterns and Trade-offs
17. Adaptive Explainable Multi-Agent Fraud Detection Framework Using Large Language Models and Hybrid Machine Learning for Mobile Money Payment Systems
18. Adaptive Explainable Multi-Agent Fraud Detection Framework Using Large Language Models and Hybrid Machine Learning for Mobile Money Payment Systems
19. A SURVEY OF DEEP LEARNING ARCHITECTURES FOR TRANSACTION AND FINANCIAL FRAUD DETECTION, ANCHORED ON THE MULTI-TASK CNN BEHAVIOURAL EMBEDDING MODEL
20. A novel deep learning-based fraud detection framework integrating autonomous PSO-GA-based feature selection, hyperparameter optimization, and explainable artificial intelligence