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
Machine Learning
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ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
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Journal of Artificial Intelligence Research
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
Training and assessing classification rules with imbalanced data
Data Mining and Knowledge Discovery
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This paper deals with an optimization of Random Forests which aims at: adapting the concept of forest for learning imbalanced data as well as taking into account user's wishes as far as recall and precision rates are concerned. We propose to adapt Random Forest on two levels. First of all, during the forest creation thanks to the use of asymmetric entropy measure associated to specific leaf class assignation rules. Then, during the voting step, by using an alternative strategy to the classical majority voting strategy. The automation of this second step requires a specific methodology for results quality assessment. This methodology allows the user to define his wishes concerning (1) recall and precision rates for each class of the concept to learn, and, (2) the importance he wants to confer to each one of those classes. Finally, results of experimental evaluations are presented.