基于聚类权的SVM解不平衡数据集分类.pdf

Computer Engineering and Applications 计算机工程与应用 2015 ，51（21 ） 133 基于聚类权重分阶段的SVM 解不平衡数据集分类 1 1 2 王超学 ，张 涛 ，马春森 1 1 2 WANG Chaoxue , ZHANG Tao , MA Chunsen 1.西安建筑科技大学 信息与控制工程学院，西安 710055 2.中国农业科学院 植物保护研究所，北京 100193 1.School of Information and Control Engineering, Xi ’an University of Architecture and Technology, Xi ’an 710055, China 2.China Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China WANG Chaoxue, ZHANG Tao, MA Chunsen. Resolution of classification for imbalanced dataset based on clus- ter-weight and grading-SVM algorithm. Computer Engineering and Applications, 2015, 51 （21 ）：133-137. 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. Key words: imbalanced dataset; weight assignment model; Support Vector Machine （SVM