Dynamic weighted voting for multiple classifier fusion:a generalized rough set method  

作　　者：Sun Liang Han Chongzhao 

机构地区：[1]School of Electronic & Information Engineering, Xi'an Jiaotong Univ. , Xi'an 710049, P. R. China [2]Inst. of Information Science & Technology, Zhengzhou 450001, P. R. China

出　　处：《Journal of Systems Engineering and Electronics》2006年第3期487-494,共8页系统工程与电子技术（英文版）

基　　金：This project was supported by the National Basic Research Programof China (2001CB309403)

摘　　要：To improve the performance of multiple classifier system, a knowledge discovery based dynamic weighted voting （KD-DWV） is proposed based on knowledge discovery. In the method, all base classifiers may be allowed to operate in different measurement/feature spaces to make the most of diverse classification information. The weights assigned to each output of a base classifier are estimated by the separability of training sample sets in relevant feature space. For this purpose, some decision tables （DTs） are established in terms of the diverse feature sets. And then the uncertainty measures of the separability are induced, in the form of mass functions in Dempster-Shafer theory （DST）, from each DTs based on generalized rough set model. From the mass functions, all the weights are calculated by a modified heuristic fusion function and assigned dynamically to each classifier varying with its output. The comparison experiment is performed on the hyperspectral remote sensing images. And the experimental results show that the performance of the classification can be improved by using the proposed method compared with the plurality voting （PV）.To improve the performance of multiple classifier system, a knowledge discovery based dynamic weighted voting （KD-DWV） is proposed based on knowledge discovery. In the method, all base classifiers may be allowed to operate in different measurement/feature spaces to make the most of diverse classification information. The weights assigned to each output of a base classifier are estimated by the separability of training sample sets in relevant feature space. For this purpose, some decision tables （DTs） are established in terms of the diverse feature sets. And then the uncertainty measures of the separability are induced, in the form of mass functions in Dempster-Shafer theory （DST）, from each DTs based on generalized rough set model. From the mass functions, all the weights are calculated by a modified heuristic fusion function and assigned dynamically to each classifier varying with its output. The comparison experiment is performed on the hyperspectral remote sensing images. And the experimental results show that the performance of the classification can be improved by using the proposed method compared with the plurality voting （PV）.

关 键 词：multiple classifier fusion dynamic weighted voting generalized rough set hyperspectral. 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]