An Improved SVM-KM Model For Imbalanced Datasets

被引：35

作者：

Deng Weiguo ^{[1
]}

Wang Li ^{[1
]}

Wang Yiyang ^{[1
]}

Qian Zhong ^{[1
]}

机构：

[1] Beihang Univ, Sch Econ & Management, Beijing, Peoples R China

来源：

2012 INTERNATIONAL CONFERENCE ON INDUSTRIAL CONTROL AND ELECTRONICS ENGINEERING (ICICEE) | 2012年

关键词：

support vector machine; k-means; different error costs; imbalanced datasets; SELECTION;

D O I：

10.1109/ICICEE.2012.35

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Support vector machine is a widely used machine learning technique. SVM-KM model can speed SVM training by eliminating non support vectors, but imbalanced datasets will affect the classification accuracy. In this paper, we proposed an improved SVM-KM model, which assign different error costs to different classes. Based on the simulation results, the improved SVM-KM model performed best for imbalanced datasets.

引用

页码：100 / 103

页数：4

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