Dynamic Fuzzy Density for Multi-classifier Fusion Algorithm

doi:10.3969/j.issn.0372-2112.2018.05.034

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Dynamic Fuzzy Density for Multi-classifier Fusion Algorithm

更新时间：2025-07-08

- Dynamic Fuzzy Density for Multi-classifier Fusion Algorithm
- Acta Electronica Sinica Vol. 46, Issue 5, Pages: 1246-1252(2018)
- 作者机构：
  
  1. 合肥工业大学计算机与信息学院,安徽,合肥,230009
  2. 情感计算与先进智能安徽省重点实验室,安徽,合肥,230009
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61672202, No.61432004, No.61502141);Key Program of NSFC-Shenzhen Joint Fund (No.U1613217)
- DOI：10.3969/j.issn.0372-2112.2018.05.034
  CLC： TP391
- Published Online：25 May 2018，
  
  Published：2018
- 稿件说明：
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Dynamic Fuzzy Density for Multi-classifier Fusion Algorithm[J]. Acta Electronica Sinica, 2018, 46(5): 1246-1252.
DOI：

Dynamic Fuzzy Density for Multi-classifier Fusion Algorithm[J]. Acta Electronica Sinica, 2018, 46(5): 1246-1252. DOI： 10.3969/j.issn.0372-2112.2018.05.034.

摘要

在分析现有模糊密度计算方法的基础上，本文从分类器的隶属度分布和输出一致性两方面探索计算模糊密度的新方法，提出一种基于决策信任度和支持度的动态模糊密度赋值方法，旨在根据各分类器识别具体目标时输出的客观信息，实时地刻画分类器在融合系统中的可靠性.在表情识别上的实验结果表明，本文方法可以有效提高模糊积分融合的决策性能，降低单分类器输出不可靠决策信息的干扰，是一种有效的多分类器融合方法.

Abstract

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.

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