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

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