Knowledge discovery method for feature-decision level fusion of multiple classifiers

作     者：孙亮 韩崇昭 Sun Liang;Han Chongzhao

作者机构：西安交通大学电子与信息工程学院 解放军信息工程大学理学院郑州450001 

出 版 物：《Journal of Southeast University(English Edition)》 (东南大学学报（英文版）)

年 卷 期：2006年第22卷第2期

页      面：222-227页

核心收录：

学科分类：081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：TheNationalBasicResearchProgramofChina(973Program)(No.2001CB309403) 

主　　题：multiple classifier fusion knowledge discovery Dempster-Shafer theory generalized rough set hyperspectral 

摘      要：To improve the performance of the multiple classifier system, a new method of feature-decision level fusion is proposed based on knowledge discovery. In the new method, the base classifiers operate on different feature spaces and their types depend on different measures of between-class separability. The uncertainty measures corresponding to each output of each base classifier are induced from the established decision tables （DTs） in the form of mass function in the Dempster-Shafer theory （DST）. Furthermore, an effective fusion framework is built at the feature-decision level on the basis of a generalized rough set model and the DST. The experiment for the classification of hyperspectral remote sensing images shows that the performance of the classification can be improved by the proposed method compared with that of plurality voting （PV）.