About this document
Review of Imbalanced Learning Techniques by maria isabel Vidal is a document available to read on EtoBox.
This paper reviews 258 peer-reviewed studies on machine learning techniques for imbalanced data, focusing on the challenges of detecting rare events in classification problems. It categorizes approaches into problem definition, data processing, and algorithmic methods, while also discussing evaluation metrics and future research trends. The goal is to provide a comprehensive overview and guidelines for researchers working with imbalanced datasets in various applications.
- Author
- maria isabel Vidal
- Language
- EN