利用证据神经网络的多分类器系统构造

和红顺 1; 韩德强 1; 2; 杨艺 3

doi:10.7652/xjtuxb201811014

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利用证据神经网络的多分类器系统构造

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- 利用证据神经网络的多分类器系统构造
- Design of Multiclassifier Systems Based on an Evidential Neural Network
- 西安交通大学学报 2018年52卷第11期页码：93-99+141
- 作者机构：
  
  1. 西安交通大学电子与信息工程学院,西安,710049
  2. 中国电子科技集团公司航天信息应用技术重点实验室,石家庄,050004
- 作者简介：
- 基金信息：
  
  : 2018-01-15。作者简介: 和红顺(1990—),男,博士生;韩德强(通信作者),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(61573275,61671370);国家“973计划”资助项目(2013CB329405);中国电子科技集团公司航天信息应用技术重点实验室高校合作课题(KX172600034)
- DOI：10.7652/xjtuxb201811014
  中图分类号： TP391
- 网络首发：2018-11-10，
  
  纸质出版：2018
- 稿件说明：
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和红顺 1, 韩德强 1, 2, 等. 利用证据神经网络的多分类器系统构造[J]. 西安交通大学学报, 2018,52(11):93-99+141.

Design of Multiclassifier Systems Based on an Evidential Neural Network[J]. 2018, 52(11): 93-99+141.
和红顺 1, 韩德强 1, 2, 等. 利用证据神经网络的多分类器系统构造[J]. 西安交通大学学报, 2018,52(11):93-99+141. DOI： 10.7652/xjtuxb201811014.

Design of Multiclassifier Systems Based on an Evidential Neural Network[J]. 2018, 52(11): 93-99+141. DOI： 10.7652/xjtuxb201811014.

摘要

为了充分利用数据信息进而提高分类正确率

提出一种证据神经网络的分类器

并据此构造了多分类器系统。首先将训练数据中的含混数据视为新类别——混合类

将原始的训练数据重组成含有混合类的训练数据

然后使用证据神经网络分类器系统用重组后含混合类的训练数据进行训练

对分类输出进行证据建模

并使用多种不同的证据组合规则实现多分类器融合。采用人工数据集和UCI数据集进行对比实验

结果表明:与其他采用神经网络的多分类器系统相比

采用证据神经网络的多分类器系统能有效提高分类正确率; 在数据集Magic 04和Waveform2上

采用提出的多分类器系统比采用投票法的神经网络多分类器系统的分类正确率分别提高了6%和10%左右。

Abstract

A new evidential neural network classifier is proposed to make full use of the data information and to improve the classification performance and an implementation of multiple classifier systems based on the new evidential neural network classifier is presented. Firstly

the ambiguous data contained in the training data are considered as a new category-compound class and the original training data are reconstructed into a new data set with compound classes. Then the evidential neural network classifiers are trained using the new data set

and classified outputs are evidentially modeled with the belief function. A variety of rules of evidence combination is used to realize multiclassifiers fusion. Experimental results on the artificial data sets and some UCI data sets and comparisons with some other existing multiclassifier systems based on neural networks show that the proposed multiclassifier systems effectively improve the classification accuracy. Especially

comparisons with the neural network multiclassifier systems based on the voting law on the data sets Magic 04 and Waveform2 show that the proposed multiclassifier systems have about 6% and 10% increases in classification accuracy

respectively.

关键词

Keywords

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