Imbalanced Data Classification Techniques

Imbalanced Data Classification Techniques is a research topic within Artificial Intelligence. Science Explorer counts 21k research works in it since 1954. 10.0% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the challenges and techniques for handling imbalanced data in classification problems. It covers methods such as SMOTE, ROC analysis, cost-sensitive learning, ensemble methods, and their applications in fraud detection. The cluster also discusses the use of precision-recall and boosting algorithms, as well as the effectiveness of random forest in addressing imbalanced datasets.

• Imbalanced Data
• Classification
• SMOTE
• ROC Analysis
• Cost-Sensitive Learning
• Ensemble Methods
• Fraud Detection
• Precision-Recall
• Boosting
• Random Forest