A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Extreme re-balancing for SVMs: a case study
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
On the Class Imbalance Problem
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 04
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Class imbalance and the curse of minority hubs
Knowledge-Based Systems
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There is an increasing interest in the design of classifiers for imbalanced problems due to their relevance in many fields, such as fraud detection and medical diagnosis. In this work we present a new classifier developed specially for imbalanced problems, where maximum F-measure instead of maximum accuracy guide the classifier design. Theoretical basis, algorithm description and real experiments are presented. The algorithm proposed shows suitability and a very good performance in imbalance scenarios and high overlapping between classes.