MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Explicitly representing expected cost: an alternative to ROC representation
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Effects of domain characteristics on instance-based learning algorithms
Theoretical Computer Science - Selected papers in honour of Setsuo Arikawa
Class imbalances versus small disjuncts
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
Using AUC and Accuracy in Evaluating Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
The Influence of Class Imbalance on Cost-Sensitive Learning: An Empirical Study
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
The class imbalance problem: A systematic study
Intelligent Data Analysis
When Overlapping Unexpectedly Alters the Class Imbalance Effects
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Balancing strategies and class overlapping
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
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In this paper, we introduce a new approach to evaluate and visualize the classifier performance in two-class imbalanced domains. This method defines a two-dimensional space by combining the geometric mean of class accuracies and a new metric that gives an indication of how balanced they are. A given point in this space represents a certain trade-off between those two measures, which will be expressed as a trapezoidal function. Besides, this evaluation function has the interesting property that it allows to emphasize the correct predictions on the minority class, which is often considered as the most important class. Experiments demonstrate the consistency and validity of the evaluation method here proposed.