Design of Multiclassifier Systems Based on an Evidential Neural Network

doi:10.7652/xjtuxb201811014

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Design of Multiclassifier Systems Based on an Evidential Neural Network

更新时间：2025-07-09

- Design of Multiclassifier Systems Based on an Evidential Neural Network
- Vol. 52, Issue 11, Pages: 93-99+141(2018)
- 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：
- DOI：10.7652/xjtuxb201811014
  CLC： TP391
- Online First：10 November 2018，
  
  Published：2018
- 稿件说明：
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Design of Multiclassifier Systems Based on an Evidential Neural Network[J]. 2018, 52(11): 93-99+141.
DOI：

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

references

周志华. 机器学习 [M]. 北京: 清华大学出版社, 2016: 171-193.

HO T, HULL J, SRIHARI S. Decision combination in multiple classifier systems [J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 1994, 16(1): 66-75.

王风华, 韩九强, 姚向华. 一种基于虹膜和人脸的多生物特征融合方法 [J]. 西安交通大学学报, 2008, 42(2): 133-137.

WANG Fenghua, HAN Jiuqiang, YAO Xianghua. Multimodal biometric fusion approach based on iris and face [J]. Journal of Xi'an Jiaotong University, 2008, 42(2): 133-137.

SIRLANTZIS K, HOQUE S, FAIRHURST M C. Diversity in multiple classifier ensembles based on binary feature quantization with application to face recognition [J]. Applied Soft Computing, 2008, 8(1): 437-445.

BREIMAN L. Bagging predictors [J]. Machine Learning, 1996, 24(2): 123-140.

焦李成, 公茂果, 王爽, 等. 自然计算、机器学习与图像理解前沿 [M]. 西安: 西安电子科技大学出版社, 2008: 184-191.

DIETTERICH T. An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization [J]. Machine Learning, 2000, 40(2): 139-157.

WINDEATT T. Diversity measures for multiple classifier system analysis and design [J]. Information Fusion, 2005, 6(1): 21-36.

KUNCHEVA L, WHITAKER C. Measures of diversity in classier ensembles and their relationship with the ensemble accuracy [J]. Machine Learning, 2003, 51(2): 181-207.

MARKO R, IGOR K. Theoretical and empirical analysis of RreliefF and RReliefF [J]. Machine Learning, 2003, 53(1/2): 23-69.

HUANG Y, SUEN C. A method of combining multiple experts for the recognition of unconstrained handwritten numerals [J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 1995, 17(1): 90-94.

XU L, KRZYZAK A, SUEN C. Methods of combining multiple classifiers and their applications to handwriting recognition [J]. IEEE Transactions on Cybernetics, 1992, 22(3): 418-435.

韩德强, 杨艺, 韩崇昭. DS证据理论研究进展及相关问题讨论 [J]. 控制与决策, 2014, 2(1): 1-11.

HAN Deqiang, YANG Yi, HAN Chongzhao. Advances in DS evidence theory and related discussions [J]. Control and Decision, 2014, 2(1): 1-11.

KUNCHEBA L. Diversity in multiple classifier systems [J]. Information Fusion, 2005, 6(1): 3-4.

SHALFER G. A mathematical theory of evidence [J]. Technometrics, 1977, 20(1): 106-109.

梁绍一, 韩德强, 韩崇昭, 等. 一种基于几何关系的多分类器差异性度量及其在多分类器系统构造中的应用 [J]. 自动化学报, 2014, 40(3): 449-458.

LIANG Shaoyi, HAN Deqiang, HAN Chongzhao. A novel diversity measure based on geometric relationship and its application to design of multiple classifier systems [J]. Acta Automatica Sinica, 2014, 40(3): 449-458.

HAN Deqiang, DEZERT J, DUAN Zhansheng. Evaluation of probability transformations of belief functions for decision making [J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2015, 46(1): 93-108.

HAN Deqiang, LIU Weibing, DESERT J, et al. A novel approach to pre-extracting support vectors based on the theory of belief functions [J]. Knowledge-Based Systems, 2016, 110: 210-223.

LIU Zhunga, PAN Quan, DESERT J, et al. Credal c-means clustering method based on belief functions [J]. Knowledge-Based Systems, 2015, 74: 119-132.

LIU Zhunga, PAN Quan, DESERT J. Evidential classifier for imprecise data based on belief functions [J]. Knowledge-Based Systems, 2013, 52: 246-257.

DENOEUX T. A k-nearest neighbor classification rule based on Dempster-Shafer theory [J]. IEEE Transactions on Systems, Man and Cybernetics, 1995, 25(5): 804-813.

DENOEUX T. Classification using belief functions: relationship between case-based and model-based approaches [J]. IEEE Transactions on Systems, Man, and Cybernetics: Part B Cybernetics, 2006, 36(6): 1395-1405.

YAGER R. On the Dempster-Shafer framework and new combination rules [J]. Information Sciences, 1987, 41(2): 93-137.

MURPHY C. Combining belief functions when evidence conflicts [J]. Information Sciences, 2000, 29(1): 1-9.

杨风暴, 王肖霞. D-S证据理论的冲突证据合成方法 [M]. 北京: 国防工业出版社, 2010: 95-155.

李巍华, 张盛刚. 基于改进证据理论及多神经网络融合的故障分类 [J]. 机械工程学报, 2010, 46(9): 93-99.

LI Weihua, ZHANG Shenggang. Fault classification based on improved evidence theory and multiple neural network fusion [J]. Journal of Mechanical Engineering, 2010, 46(9): 93-99.

DUDANI S A. The distance-weighted k-nearest-neighbor rule [J]. IEEE Transactions on Systems Man Cybernetics, 1976, 6(4): 325-327.

CHAWAL N, BOWYER K, HALL L, et al. SMOTE: synthetic minority over-sampling technique [J]. Journal of Artificial Intelligence Research, 2002, 16(1): 321-357.

NADAL C, LEGAULT R, SUEN C. Complementary algorithms for the recognition of totally unconstrained handwritten numerals [C]∥Proceedings of the International Conference on Patten Recognition. Piscataway, NJ, USA: IEEE, 1990: 443-449.

HULL J, SRIHARI S N, CPHEN E. A blackboard-based approach to handwritten ZIP code recognition [C]∥Proceedings of the International Conference on Pattern Recognition. Piscataway, NJ, USA: IEEE, 1988: 111-113.

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