To improve the classification performance of minority class, a over-sampling based on negative immune principle is proposed. In this approach, the negative immune algorithm is induced to generate a set of resprentive detectors to implement the overlapping of minority class space based on learning majority samples. The centers of resprentive
detectors are regarded as the synthetic minority samples in order to resolve the imbalance problem. The majority samples are used to generate the synthetic minority samples, which can address the problem of synthetic minority oversampling
technique (SMOTE)’s lacking the ability of overlapping whole minority space using the existing minority samples. Comparing the performance of the proposed approach with SMOTE and other improved algorithms, the experimental results show that the proposed method can not only effectively improve the classification performance of minority samples, but also significantly enhance the whole classification performance.