Optimizing the Classification Accuracy of Imbalanced Dataset Based on SVM

摘要：

A dataset can be called imbalanced if at least one class of the data is represented by significantly less number of samples than the others. Imbalanced data generally exists in the real world. The classification performance of traditional machine learning algorithm is hampered in the classification tasks of imbalanced dataset. Support Vector Machines (SVM) is a new kind of machine learning method based on structural risk minimization principle and has had the best performance so far in several challenging applications. This paper summarizes the applications of SVM in imbalance dataset first and then presents some main improved methods which greatly improved the performance of classification in imbalanced dataset.

关键词： SVM imbalanced dataset machine learning

作者: Zhang Sheng Shang Xiuyu Wang Wei Huang Xiuli

作者单位: College of Mathematics, Physics and Information Engineering Zhejiang Normal University Jinhua China Department of Educational Technology Nanjing Normal University Nanjing China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 338-341

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）