Abstract: For the issue of classification on imbalanced datasets, this paper presents an improved SVM-KNN classification algorithm. On this basis, an ensemble learning model is proposed. This model employs limited sampling to segment the majority class samples, re-combines the subset of majority class samples with the minority class samples, obtains several basic classifiers by training the combined subset based on improved SVM-KNN. These basic classifiers are integrated. Experimental results on UCI dataset show that this ensemble learning model has satisfactory performance when dealing with issue of classification on imbalanced datasets.

Key words: imbalanced data, ensemble learning model, basic classifier, improved Support Vector Machine-K Nearest Neighbor（SVM-KNN）, UCI dataset

摘要： 针对非平衡数据分类问题，提出了一种改进的SVM-KNN分类算法，在此基础上设计了一种集成学习模型。该模型采用限数采样方法对多数类样本进行分割，将分割后的多数类子簇与少数类样本重新组合，利用改进的SVM-KNN分别训练，得到多个基本分类器，对各个基本分类器进行组合。采用该模型对UCI数据集进行实验，结果显示该模型对于非平衡数据分类有较好的效果。

关键词: 非平衡数据, 集成学习模型, 基本分类器, 改进的支持向量机-K最近邻（SVM-KNN）, UCI 数据集