Machine Learning for the Detection of Oil Spills in Satellite Radar Images
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
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
Robust Classification for Imprecise Environments
Machine Learning
Learning Decision Trees Using the Area Under the ROC Curve
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Predicting rare classes: can boosting make any weak learner strong?
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
BRACID: a comprehensive approach to learning rules from imbalanced data
Journal of Intelligent Information Systems
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Imbalanced data learning has recently begun to receive much attention from research and industrial communities as traditional machine learners no longer give satisfactory results. Solutions to the problem generally attempt to adapt standard learners to the imbalanced data setting. Basically, higher weights are assigned to small class examples to avoid their being overshadowed by the large class ones. The difficulty determining a reasonable weight for each example remains. In this work, we propose a scheme to weight examples of the small class based solely on local data distributions. The approach is for categorical data, and a rule learning algorithm is constructed taking the weighting scheme into account. Empirical evaluations prove the advantages of this approach.