Study of Integration Method Based on Dynamically Selected Support Vector Machine and Simulation
This paper proposes a Support Vector Machine integration methodology, which is based on dynamic selection method. Using FCM-SD algorithm, an improved method from FCM and closeness degree algorithms, the effective neighborhood of the discriminated sample is determined. Furthermore, based on the accuracies of segment classifications, a set of optimal individual classifiers are selected. Finally a weighted majority vote method is used to integrate the selected classifiers. Simulation results show that the proposed method reduces the complexity of the Integrated Classification model. It effectively improves the classification performance as well.
multiple classifiers integration cluster closeness degree
Xiaoyan Lu Xiangshen Li
Department of Computer Teaching Shan xi Medical University Taiyuan, China
国际会议
太原
英文
470-474
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）