Editorial: special issue on learning from imbalanced data sets
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 class imbalance problem: A systematic study
Intelligent Data Analysis
Evaluating misclassifications in imbalanced data
ECML'06 Proceedings of the 17th European conference on Machine Learning
Beyond accuracy, f-score and ROC: a family of discriminant measures for performance evaluation
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Exploring the performance of resampling strategies for the class imbalance problem
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Adjusted F-measure and kernel scaling for imbalanced data learning
Information Sciences: an International Journal
Hi-index | 0.00 |
This paper introduces a new metric, named Index of Balanced Accuracy , for evaluating learning processes in two-class imbalanced domains. The method combines an unbiased index of its overall accuracy and a measure about how dominant is the class with the highest individual accuracy rate. Some theoretical examples are conducted to illustrate the benefits of the new metric over other well-known performance measures. Finally, a number of experiments demonstrate the consistency and validity of the evaluation method here proposed.