Evidential combination of augmented multi-source of information based on domain adaptation

作     者：Linqing HUANG Zhunga LIU Quan PAN Jean DEZERT Linqing HUANG;Zhunga LIU;Quan PAN;Jean DEZERT

作者机构：School of Automation Northwestern Polytechnical University ONERA-The French Aerospace Lab 

出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))

年 卷 期：2020年第63卷第11期

页      面：38-55页

核心收录：

学科分类：0810[工学-信息与通信工程] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by National Natural Science Foundation of China (Grant Nos. 61672431, 61790552, 61790554, 61701409) Shaanxi Science Fund for Distinguished Young Scholars (Grant No. 2018JC-006) Fundamental Research Funds for the Central Universities 

主　　题：information fusion domain adaptation evidence theory belief functions pattern classification 

摘      要：In the applications of domain adaptation(DA), there may exist multiple source domains, and each source domain usually provides some auxiliary information for object classification. The combination of such complementary knowledge from different source domains is helpful for improving the accuracy. We propose an evidential combination of augmented multi-source of information(ECAMI) method. The information sources are augmented at first by merging several randomly selected source domains to generate extra auxiliary information. We can obtain one piece of classification result with the assistance of each information source based on DA. Then these multiple classification results are combined by belief functions theory, which is expert at dealing with the uncertain information. Nevertheless, the classification results derived from different information sources may have different weights. The optimal weights are calculated by minimizing an given error criteria defined by the distance between the combination result and the ground truth using some training data. For each object, the augmented information sources will produce multiple classification results that will be discounted by the learnt weights under the belief functions framework. Then the combination of these discounted results is employed to make the final class decision. The effectiveness of ECAMI is evaluated with respect to some related methods based on several real data sets, and the experimental results show that ECAMI can significantly improve the classification accuracy.