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。
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.
出处
《计算机工程与应用》
CSCD
2012年第29期119-123,219,共6页
Computer Engineering and Applications
基金
国家自然科学基金(No.61175048)