Title :
An Improved SVM-KM Model for Imbalanced Datasets
Author :
Weiguo, Deng ; Li, Wang ; Yiyang, Wang ; Zhong, Qian
Author_Institution :
Sch. of Econ. & Manage., Beihang Univ., Beijing, China
Abstract :
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.
Keywords :
learning (artificial intelligence); pattern classification; support vector machines; imbalanced datasets; improved SVM-KM model; machine learning technique; pattern classification; support vector machine; Industrial control; different error costs; imbalanced datasets; k-means; support vector machine;
Conference_Titel :
Industrial Control and Electronics Engineering (ICICEE), 2012 International Conference on
Conference_Location :
Xi´an
Print_ISBN :
978-1-4673-1450-3
DOI :
10.1109/ICICEE.2012.35
