面向不均衡数据的多分类集成算法

为解决不均衡多分类问题,提出了一种基于采样和特征选择的不均衡数据集成分类算法(IDESF).基分类器的多样性会影响集成算法的分类性能,所以IDESF算法对数据集进行有放回采样+SMOTE的两阶段采样.两阶段采样在保证所得数据集中样本合理性的基础上,增加数据集间的差异性以此隐式地提高基分类器的多样性.两阶段采样同样可以平...

Multi-classification Ensemble Algorithm for Imbalanced Data

To solve the problem of imbalanced multi-classification, an ensemble classification algorithm（IDESF） based on sampling and feature selection is proposed. The diversity of base classifier affects the classification performance of the ensemble algorithm. Therefore, the IDESF algorithm first performs a two-stage sampling of the data set with replacement sampling+SMOTE. On the basis of ensuring the rationality of the samples in the obtained data set, the two-stage sampling increases the difference among the data sets so as to improve the diversity of the base classifier implicitly. In addition, two-stage sampling can also balance the data distribution and prevent the classifier from favoring the majority classes. Based on two-stage sampling, IDESF algorithm introduces data cleaning and feature selection methods to further improve the classification performance of the algorithm. Finally, the results show that the algorithm can obtain higher AUCarea and G-mean and has better classification effect.