Over-sampling algorithm based on preliminary classification in imbalanced data sets learning
不均衡数据集学习中基于初分类的过抽样算法

To significantly improve the classification performance of the minority class, an over-sampling algorithm based on preliminary classification was presented. Firstly, preliminary classification was made on the test data in order to save the useful information of the majority class as much as possible, Then the test data that were predicted to belong to minority class were reclassified to improve the classification performance of the minority class. Using the data sets provided by University of California, Irvine, the new algorithm was compared with synthetic minority over-sampling technique and under-sampling method. The experimental results show that the new algorithm performs better than the others in terms of the classification performance of the minority class and majority class.