计算机工程与应用Issue(21)：133-137,5.DOI:10.3778/j.issn.1002-8331.1311-0145
基于聚类权重分阶段的SVM解不平衡数据集分类
Resolution of classification for imbalanced dataset based on clus-ter-weight and grading-SVM algorithm
摘要
Abstract
Based on analyzing the shortages of SVM(Support Vector Machine)algorithm in solving classification problems on imbalanced dataset, a novel SVM approach based on cluster-weight technology and based-grading SVM classifier(short as WSVM)is presented in this paper that considers the uneven distribution of training sample between classes and within classes. The specific steps are as follows:when preprocessing, it uses K-means algorithm based on weight assignment model to obtain the weights of the majority samples. Classification is consisted of three phases. It selects the located in each cluster boundary majority samples, which is equal with the minority samples in quantity, then classifies the minority samples and selects samples, and adjusts the initial classifier through the unselected majority samples. When it comes to satisfy the explicit stopping criteria, the final classifier is got. A large amount of experiments by the UCI dataset show that WSVM can significantly improve the identification rate of the minority samples and overall classification performance.关键词
不平衡数据集/权重分配模型/支持向量机(SVM)Key words
imbalanced dataset/weight assignment model/Support Vector Machine(SVM)分类
信息技术与安全科学引用本文复制引用
王超学,张涛,马春森..基于聚类权重分阶段的SVM解不平衡数据集分类[J].计算机工程与应用,2015,(21):133-137,5.基金项目
国家自然科学基金（No.31170393）；陕西省自然科学基金（No.2012JM8023）；陕西省教育厅自然科学基金专项（No.12JK0726）。 （）
