Overview: Offers a comprehensive review of imbalanced learning widely used worldwide in many real applications, such as fraud detection, disease diagnosis, etc.Provides the user with the required background and.This? book provides a general and comprehensible?overview of?? imbalanced learning.? It contains a formal description of a problem, and focuses on its main features, and the most relevant proposed solutions. Additionally, it considers?the different scenarios in Data Science for which the imbalanced classification can?create a real challenge.?.This book stresses the gap with standard cla
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