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
Robust Classification for Imprecise Environments
Machine Learning
Learning When Negative Examples Abound
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Improving Minority Class Prediction Using Case-Specific Feature Weights
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Rule Quality Measures Improve the Accuracy of Rule Induction: An Experimental Approach
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
ELEM2: A Learning System for More Accurate Classifications
AI '98 Proceedings of the 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
BRACID: a comprehensive approach to learning rules from imbalanced data
Journal of Intelligent Information Systems
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We present our experience in applying a rule induction technique to an extremely imbalanced pharmaceutical data set. We focus on using a variety of performance measures to evaluate a number of rule quality measures. We also investigate whether simply changing the distribution skew in the training data can improve predictive performance. Finally, we propose a method for adjusting the learning algorithm for learning in an extremely imbalanced environment. Our experimental results show that this adjustment improves predictive performance for rule quality formulas in which rule coverage makes positive contributions to the rule quality value.