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Combination of classifiers with incomplete frames of discernment

Chinese Journal of Aeronautics 2022年第5期35卷 145-157页

作者： Zhunga LIU Jingfei DUAN Linqing HUANG Jean DEZERT Yongqiang ZHAO School of Automation Northwestern Polytechnical UniversityXi’an 710072China ONERA–The French Aerospace Lab Palaiseau 91761France

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.

关键词： abnormal object Belief functions Classifier fusion Evidence theory Detection

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