不平衡数据严重影响了传统分类算法的性能,导致少数类的识别率降低.提出一种基于邻域特征的混合抽样技术,该技术根据样本邻域中的类别分布特征来确定采样权重,进而采用混合抽样的方法来获得平衡的数据集;然后采用一种基于局部置信度的动态集成方法,通过分类学习生成基分类器,对于每个检验的样本,根据局部分类精度动态地选择最优的基分类器进行组合.通过UCI标准数据集上的实验表明,该方法能够同时提高不平衡数据中少数类和多数类的分类精度.%The class imbalance problems severely affect the performance of the traditional classification algorithm,leading to decrease the recognition rate of the minority.In order to solve this problem,a hybrid sampling technology based on neighborhood characteristic was proposed to enhance the classification accuracy of minority class.This technology changes the sampling weight according to the class distribution in the samples neighborhood,and uses the hybrid sampling to obtain the balanced data subset.Then the base classifiers are generated,for each test sample,a dynamic ensemble method based on local confidence is proposed to select the optimal base classifier sets.The experiments on UCI datasets show that the method has high classification accuracy rate of both minority and majority class for imbalance datasets.
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