Imbalanced data classification algorithm based on boosting and cascade model
Xiaolong Zhang, Chao Cheng · 2012
Traditional classification algorithms are difficult in dealing with imbalance data. This paper proposes a classification algorithm called CascadeBoost, which combines with the advantages of boosting algorithm and cascade model that can learn imbalance data. Cascade model allows the pre-training data to be balanced by gradually reducing the number of the major class; and then the most rich information samples based on the weight distribution can be gradually selected using boosting algorithm. The experimental results show that the proposed method obtains better performance compared to other methods.