Learning object from small and imbalanced dataset with Boost-BFKO

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 samples from the minority class, and rebalances the total weights of the different classes in the updated training dataset. Experiments on Caltech 101 database showed that our method achieves a desirable performance when only a few training samples are available for binary classification and multiple object classification.