Dynamic Fuzzy Density for Multi-classifier Fusion Algorithm

被引：0
|
作者：
Li Y.-Q. [1 ,2 ]
Ren F.-J. [1 ,2 ]
Hu M. [1 ,2 ]
机构：
[1] School of Computer and Information, Hefei University of Technology, Hefei, 230009, Anhui
[2] Affective Computing and Advanced Intelligent Machines Anhui Key Laboratory, Hefei, 230009, Anhui
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2018年 / 46卷 / 05期
关键词：
Facial expression recognition; Fuzzy density; Fuzzy integral; Multi-classifier fusion;
D O I：
10.3969/j.issn.0372-2112.2018.05.034
中图分类号：
学科分类号：
摘要：
Based on the analysis of the existing fuzzy density calculation methods, this paper explores a new method of calculating fuzzy density from the membership degree distribution and output consistency of the classifiers, and proposes a dynamic fuzzy density assignment method based on decision trust and support degree, which aims to describe the reliability of the classifier in the fusion system in real time according to the objective information output when each classifier identifies the specific target. The experimental results on facial expression recognition show that the proposed method can effectively improve the decision performance of fuzzy integral fusion and reduce the interference of unreliable decision information output by single classifier, which is an effective multi-classifier fusion method. © 2018, Chinese Institute of Electronics. All right reserved.
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页码：1246 / 1252
页数：6
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