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

特征-决策层多分类器融合的知识发现方法(英文)

作　　者：孙亮[1,2] 韩崇昭[1] 

机构地区：[1]西安交通大学电子与信息工程学院 [2]解放军信息工程大学理学院,郑州450001

出　　处：《Journal of Southeast University(English Edition)》2006年第2期222-227,共6页东南大学学报（英文版）

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

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）.为进一步提高多分类器系统的分类性能,提出了一种基于知识发现的特征决策层多分类器融合新方法.各分类器工作于具有互补分类信息的不同特征空间且其类型由不同的类间可分性度量决定.各分类器输出的不确定性度量从建立的多个决策表中导出,并具有条件mass函数的形式.进而基于广义粗集模型和Dempster-Shafer理论(DST)构造了一种新颖的特征决策层融合框架.高光谱遥感图像的分类实验表明,与多数表决融合(PV)相比,所提出的方法可有效提高多分类器系统的分类性能.

关 键 词：multiple classifier fusion knowledge discovery Dempster-Shafer theory generalized rough set HYPERSPECTRAL 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]