Combination of classifiers with incomplete frames of discernment

作     者：Zhunga LIU Jingfei DUAN Linqing HUANG Jean DEZERT Yongqiang ZHAO Zhunga LIU;Jingfei DUAN;Linqing HUANG;Jean DEZERT;Yongqiang ZHAO

作者机构：School of AutomationNorthwestern Polytechnical UniversityXi’an 710072China ONERA–The French Aerospace LabPalaiseau 91761France 

出 版 物：《Chinese Journal of Aeronautics》 (中国航空学报（英文版）)

年 卷 期：2022年第35卷第5期

页      面：145-157页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 080203[工学-机械设计及理论] 0835[工学-软件工程] 0802[工学-机械工程] 0825[工学-航空宇航科学与技术] 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：partially supported by National Natural Science Foundation of China(Nos.U20B2067,61790552,61790554) Shaanxi Science Fund for Distinguished Young Scholars,China(No.2018JC-006) 

主　　题：Abnormal object Belief functions Classifier fusion Evidence theory Detection 

摘      要：The methods for combining multiple classifiers based on belief functions require to work with a common and complete(closed)Frame of Discernment(Fo D)on which the belief functions are defined before making their *** theoretical requirement is however difficult to satisfy in practice because some abnormal(or unknown)objects that do not belong to any predefined class of the Fo D can appear in real classification *** classifiers learnt using different attributes information can provide complementary knowledge which is very useful for making the classification but they are usually based on different Fo *** order to clearly identify the specific class of the abnormal objects,we propose a new method for combination of classifiers working with incomplete frames of discernment,named CCIF for *** is a progressive detection method that select and add the detected abnormal objects to the training data *** one pattern can be considered as an abnormal object by one classifier and be committed to a specific class by another one,a weighted evidence combination method is proposed to fuse the classification results of multiple *** new method offers the advantage to make a refined classification of abnormal objects,and to improve the classification accuracy thanks to the complementarity of the *** experimental results are given to validate the effectiveness of the proposed method using real data sets.