A Fast Two-Stage Classification Method of Support Vector Machines
Classification of high-dimensional data generally requires enormous processing time.In this paper,we present a fast two-stage method of support vector machines,which includes feature reduction algorithm and a fast multiclass method.First, principal component analysis is applied to the data for feature reduction and decorrelation,and then a feature selection method is used to further reduce feature dimensionality.The criterion based on Bhattacharyya distance is revised to get rid of influence of some binary problems with large distance.Moreover,a simple method is proposed to reduce the processing time of multiclass problems,where one binary SVM with the fewest support vectors SVs)will be selected iteratively to exclude the less similar class until the final result is obtained.Experimented with the hyperspectral data 92AV3C,the results demonstrate that the proposed method can achieve a much faster classification and preserve the high classification accuracy of SVMs.
Jin Chen Cheng Wang Runsheng Wang
ATR Laboratory,School of Electronic Science and Engineering National University of Defense Technology 47 Yanwachi,Changsha 410073,China
国际会议
2008 IEEE International Conference on Onformation and Automation(IEEE 信息与自动化国际会议)
张家界
英文
869-872
2008-06-20（万方平台首次上网日期，不代表论文的发表时间）