基于支持向量机的不平衡样本集分类算法

SVM-BASED IMBALANCED DATA CLASSIFICATION ALGORITHM

提出基于支持向量机的不平衡样本集分类算法,以支持向量机为基础,利用重采样技术及特征子空间等相关理论,通过分层抽样方法和重采样技术,分别对不平衡数据集的样本底层特征和样本数量进行平衡,在不同数据集上进行实验,实验表明该方法能有效提高不平衡数据分类的准确度.

An imbalanced data classification method which is based on the SVM algorithm is proposed in this paper. Resampling technology and feature subspace and other related theories are used and the underlying characteristics of the imbalanced data distribution and the sample size are balanced respectively by using stratified sampling method and resampling technology. Experiments are carried out on different data sets and the results show that the algorithm proposed can improve the accuracy of imbalanced data classification effectively.