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Learning object from small and imbalanced dataset with Boost-BFKO
2008
2008 IEEE International Conference on Multimedia and Expo
One of the main drawbacks of boosting is its overfitting and poor predictive accuracy when the training dataset is small and imbalanced. In this paper, we introduce a novel learning algorithm Boost-BFKO, which combines boosting and data generation. It is suitable for small and imbalanced training datasets. To enlarge training sets, Boost-BFKO uses the adaptive Balanced Feature Knockout procedure (BFKO) to generate new synthetic samples. To enrich the training sets, Boost-BFKO selects seed
doi:10.1109/icme.2008.4607718
dblp:conf/icmcs/ZhuangZTY08
fatcat:snk2f4jlqndsnh3niu6qh7ms6y