Imbalanced Data Classification Techniques
Imbalanced Data Classification Techniques is a topic indexed in ScholarIQ from OpenAlex.
What is known about Imbalanced Data Classification Techniques?
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.
How many works does Imbalanced Data Classification Techniques have?
Imbalanced Data Classification Techniques has 39,625 works in the ScholarIQ index. The count is the OpenAlex total, not the number of papers listed on this page.
How many citations does Imbalanced Data Classification Techniques have?
Imbalanced Data Classification Techniques has 600,594 citations in the OpenAlex counts ScholarIQ stores.
What is the OpenAlex record for Imbalanced Data Classification Techniques?
The OpenAlex for Imbalanced Data Classification Techniques is on the source record.