An imbalanced data classification algorithm based on boosting
Qiujie Li, Yaobin Mao, Wang Zhi-quan · Chinese Control Conference · 2011
It is currently a hot research topic that how to design appropriate learning algorithms to be applied to imbalanced data classification. This paper aims to investigate imbalanced data classification based on boosting and a weight-sampling boosting is proposed. The naive loss function of boosting is modified by the sampling function, which makes the learned classifier focus on correct classification of positive samples. The experimental results performed on UCI data sets have shown that our algorithm outperforms naive boosting and previous algorithms in the problem of imbalanced data classification.