Algorithm and application of optimal multi-classifier combination based on evidence theory

DU Hailian; LÜ Feng; XIN Tao; DU Ni

doi:10.3969/j.issn.0438-1157.2012.09.032

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Algorithm and application of optimal multi-classifier combination based on evidence theory

更新时间：2025-12-10

- Algorithm and application of optimal multi-classifier combination based on evidence theory
- CIESC Journal Issue 9, Pages: 2877-2881(2012)
- 作者机构：
  
  1. 河北师范大学职业技术学院,河北,石家庄,050024
  2. 河北师范大学物理科学与信息工程学院,河北,石家庄,050024
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0438-1157.2012.09.032
  CLC： P391
- Online First：05 September 2012，
  
  Published：05 September 2012
- 稿件说明：
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DU Hailian, LÜ Feng, XIN Tao, et al. Algorithm and application of optimal multi-classifier combination based on evidence theory[J]. CIESC Journal, 2012, (9): 2877-2881.
DOI：

DU Hailian, LÜ Feng, XIN Tao, et al. Algorithm and application of optimal multi-classifier combination based on evidence theory[J]. CIESC Journal, 2012, (9): 2877-2881. DOI： 10.3969/j.issn.0438-1157.2012.09.032.

摘要

多分类器融合方法在融合过程中需要对各分类器的输出进行加权

常见的加权方法是通过各分类器的分类正确率计算分类器的加权

但是这种方法得出的权值常常是不精确的。针对这个问题

给出了一种确定各个多分类器权值的方法。首先

通过训练集样本得出分类器系统的输出向量。然后

依据多分类器系统的输出向量与训练集样本的类别向量之间的距离最小提出一种权值的优化学习算法。最后

将得出的权值用于多分类器系统对故障类别的判定。实验结果表明

提出的方法提高了分类的正确率

能更好地判别出故障的类别。

Abstract

In the multi-classifier system

how to determine the weights of individual classifier in order to get more accurate results becomes a question that need to be solved.An optimal weight learning method is presented in this paper.First

the training samples are respectively input into the multi-classifier system based on Dempster-Shafer theory in order to obtain the output vector.Then the error is calculated by means of figuring up the distance between the output vector and class vector of corresponding training sample

and the objective function is defined as mean-square error of all the training samples.The optimal weight vector is obtained by means of minimizing the objective function.Finally

new samples are classified according to the optimal weight vector.The effectiveness of this method is illustrated by the UCI standard data set and electric actuator fault diagnostic experiment.

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