Combination of classifiers with incomplete frames of discernment
| Autor: | Zhun-ga Liu, Jean Dezert, Linqing Huang, Yongqiang Zhao, Jingfei Duan |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
0209 industrial biotechnology
Computer science business.industry Mechanical Engineering Frame (networking) Aerospace Engineering Pattern recognition 02 engineering and technology Object (computer science) 01 natural sciences Class (biology) 010305 fluids & plasmas ComputingMethodologies_PATTERNRECOGNITION 020901 industrial engineering & automation Complementarity (molecular biology) 0103 physical sciences Classifier (linguistics) Fuse (electrical) Discernment Artificial intelligence Set (psychology) business |
| Zdroj: | Chinese Journal of Aeronautics. 35:145-157 |
| ISSN: | 1000-9361 |
| DOI: | 10.1016/j.cja.2021.04.020 |
| Popis: | The methods for combining multiple classifiers based on belief functions require to work with a common and complete (closed) Frame of Discernment (FoD) on which the belief functions are defined before making their combination.This theoretical requirement is however difficult to satisfy in practice because some abnormal (or unknown) objects that do not belong to any pre-defined class of the FoD can appear in real classification applications. The 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 FoDs. In 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 short. This is a progressive detection method that select and add the detected abnormal objects to the training data set. Because 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 classifiers. This 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 classifiers. Some experimental results are given to validate the effectiveness of the proposed method using real data sets. |
| Databáze: | OpenAIRE |
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