计算机工程与应用2012，Vol.48Issue(29)：119-123,219,6.DOI:10.3778/j.issn.1002-8331.2012.29.024
一种用于非平衡数据分类的集成学习模型
Ensemble learning model for imbalanced data classification
摘要
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.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Key words
imbalanced data/ ensemble learning model/ basic classifier/ improved Support Vector Machine-K Nearest Neighbor (SVM-KNN)/ UCI dataset分类
信息技术与安全科学引用本文复制引用
焦盛岚,杨炳儒,翟云,赵万里..一种用于非平衡数据分类的集成学习模型[J].计算机工程与应用,2012,48(29):119-123,219,6.基金项目
国家自然科学基金(No.61175048). （No.61175048）
