Research on classifying technique for imbalanced dataset based on Support Vector Machines

摘要：

It is shown that SVM can be ineffective in classifying the minority samples, when it is applied to the problem of learning from imbalanced datasets. To remedy this problem, this paper analyzes the true reason of negative effect to SVM classifier caused by data imbalance firstly. Based on this, a new method of shifting classifying hyperplane in the feature space is proposed, and its implementation method—Boundary Movement based on Sample Cutting Technique (BMSCT) is also described. Through theoretical analysis and empirical study, we show that our method augments the classification accuracy rate effectively without increasing the computation complexity.

关键词： SVM Imbalanced dataset Shifting classifying hyperplane BMSCT

作者: Yang Zhi-ming Peng Yu Peng Xi-yuan

作者单位: Department of Automatic Test and Control, Harbin Institute of Technology

会议类型: 国际会议

会议名称: 第五届仪器科学与技术国际学术会议(ISIST 2008)Fifth International Symposium on Instrmentation Science and Technology

会议地点: 沈阳

会议语种:英文

页码: 1-6

在线出版日期: 2008-09-15（万方平台首次上网日期，不代表论文的发表时间）