基于推土机距离的证据冲突强度量方法

doi:10.11959/j.issn.1000-436x.2022094

通信学报 ›› 2022, Vol. 43 ›› Issue (5): 204-213.doi: 10.11959/j.issn.1000-436x.2022094

基于推土机距离的证据冲突强度量方法

王欣¹^,², 付威¹

¹ 黑龙江大学自动化系，黑龙江哈尔滨 150080
² 黑龙江省信息融合估计与检测重点实验室，黑龙江哈尔滨 150080

修回日期:2022-04-07 出版日期:2022-05-25 发布日期:2022-05-01
作者简介:王欣（1978- ），男，河北保定人，博士，黑龙江大学副教授、硕士生导师，主要研究方向为信息融合决策与估计、不确定性系统推理与建模
付威（1995- ），男，山西太原人，黑龙江大学硕士生，主要研究方向为信息融合决策与不确定性推理
基金资助:
国家自然科学基金资助项目(61573132);黑龙江省自然科学基金重点资助项目(ZD2021F003);联合引导基金资助项目(LH2020G008);黑龙江省省属高等学校基本科研业务费基础研究基金资助项目(KJCX201809)

Strong measurement method of evidence conflict based on earth mover’s distance

Xin WANG¹^,², Wei FU¹

¹ Department of Automation, Heilongjiang University, Harbin 150080, China
² Key Laboratory of Information Fusion Estimation and Detection in Heilongjiang Province, Harbin 150080, China

Revised:2022-04-07 Online:2022-05-25 Published:2022-05-01
Supported by:
The National Natural Science Foundation of China(61573132);Key Project of Natural Science Foundation of Heilongjiang Province(ZD2021F003);Joint Guidance Project(LH2020G008);Basic Research Fund in Higher Educational Institutions of Heilongjiang Province(KJCX201809)

摘要/Abstract

摘要：

针对D-S（Dempster-Shafer）证据理论中的冲突因子k不能有效度量证据之间的冲突程度的问题，首先提出了证据冲突强度量函数需要满足的期望特征，然后提出了一种新的基于推土机距离的证据冲突度量方法。该方法不要求不同证据有相同的焦元数量，并且可以直接计算含有非单子集命题的证据冲突。理论和实验表明，所提方法给出的冲突度量大小能够正确表征2个证据间冲突的程度，并具有证据冲突强度量函数的所有期望特征，表明所提方法是一种有效的证据冲突强度量方法。

关键词: D-S证据理论, 证据冲突强度量函数, 期望特征, 推土机距离

Abstract:

Aiming at the problem that the conflict factor k in D-S evidence theory cannot effectively measure the degree of conflict between two bodies of evidence (BoEs), the expected features of evidence conflict strong measurement function (ECSMF) were proposed, and then a new method of evidence conflict measurement based on earth mover’s distance (EMD)was put forward.The same number of focal elements between different BoEs was not required and the evidence conflict with non-singleton propositions could be directly calculate.The proposed method did not require The theory and experiments show that the size of conflict measure proposed can correctly represent the degree of conflict between two BoEs, and has all the expected features of ECSMF, which is an effective method of evidence conflict strong measurement.

Key words: D-S evidence theory, evidence conflict strong measurement function, expected feature, earth mover’s distance

中图分类号:

TP274

王欣, 付威. 基于推土机距离的证据冲突强度量方法[J]. 通信学报, 2022, 43(5): 204-213.

Xin WANG, Wei FU. Strong measurement method of evidence conflict based on earth mover’s distance[J]. Journal on Communications, 2022, 43(5): 204-213.

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参考文献 26

[1]	NAGARANI N , VENKATAKRISHNAN P , BALAJI N . Unmanned Aerial vehicle’s runway landing system with efficient target detection by using morphological fusion for military surveillance system[J]. Computer Communications, 2020,151: 463-472.
[2]	MUZAMMAL M , TALAT R , SODHRO A H ,et al. A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks[J]. Information Fusion, 2020,53: 155-164.
[3]	LIN K , LI Y H , SUN J C ,et al. Multi-sensor fusion for body sensor network in medical human-robot interaction scenario[J]. Information Fusion, 2020,57: 15-26.
[4]	JIANG M Q , LIU J P , ZHANG L ,et al. An improved stacking framework for stock index prediction by leveraging tree-based ensemble models and deep learning algorithms[J]. Physica A:Statistical Mechanics and Its Applications, 2020,541:122272.
[5]	FEI L G , DENG Y . A new divergence measure for basic probability assignment and its applications in extremely uncertain environments[J]. International Journal of Intelligent Systems, 2019,34(4): 584-600.
[6]	DEN?UX T , SHENOY P P . An interval-valued utility theory for decision making with Dempster-Shafer belief functions[J]. International Journal of Approximate Reasoning, 2020,124: 194-216.
[7]	PAN Y , ZHANG L M , WU X G ,et al. Multi-classifier information fusion in risk analysis[J]. Information Fusion, 2020,60: 121-136.
[8]	BOUKEZZOULA R , COQUIN D , NGUYEN T L ,et al. Multi-sensor information fusion:combination of fuzzy systems and evidence theory approaches in color recognition for the NAO humanoid robot[J]. Robotics and Autonomous Systems, 2018,100: 302-316.
[9]	GHOSH N , PAUL R , MAITY S ,et al. Fault matters:sensor data fusion for detection of faults using Dempster-Shafer theory of evidence in IoT-based applications[J]. Expert Systems With Applications, 2020,162:113887.
[10]	FU W , YU S , WANG X . A novel method to determine basic probability assignment based on adaboost and its application in classification[J]. Entropy (Basel,Switzerland), 2021,23(7): 812.
[11]	ZADEH L A . Review of Shafer’s:a mathematical theory of evidence[J]. AI Magazine, 1984,5: 81-83.
[12]	宋亚飞, 王晓丹, 雷蕾 ,等. 基于相关系数的证据冲突度量方法[J]. 通信学报, 2014,35(5): 95-100.
	SONG Y F , WANG X D , LEI L ,et al. Measurement of evidence conflict based on correlation coefficient[J]. Journal on Communications, 2014,35(5): 95-100.
[13]	孙贵东, 关欣, 衣晓 ,等. 冲突证据的相关系数度量方法[J]. 通信学报, 2018,39(12): 30-39.
	SUN G D , GUAN X , YI X ,et al. Correlation coefficient measurement for conflict evidence[J]. Journal on Communications, 2018,39(12): 30-39.
[14]	LIU W R . Analyzing the degree of conflict among belief functions[J]. Artificial Intelligence, 2006,170(11): 909-924.
[15]	郭兴林, 孙振晓, 周昱瑶 ,等. 基于 Pignistic 概率转换和奇异值分解的证据冲突度量方法[J]. 通信学报, 2021,42(4): 150-157.
	GUO X L , SUN Z X , ZHOU Y Y ,et al. Evidence conflict measurement method based on Pignistic probability transformation and singular value decomposition[J]. Journal on Communications, 2021,42(4): 150-157.
[16]	CAI Q X , GAO X Z , DENG Y . Pignistic belief transform:a new method of conflict measurement[J]. IEEE Access, 2020,8: 15265-15272.
[17]	JOUSSELME A L , GRENIER D , BOSSé é , . A new distance between two bodies of evidence[J]. Information Fusion, 2001,2(2): 91-101.
[18]	邓勇, 王栋, 李齐 ,等. 一种新的证据冲突分析方法[J]. 控制理论与应用, 2011,28(6): 839-844.
	DENG Y , WANG D , LI Q ,et al. A new method to analyze evidence conflict[J]. Control Theory ＆ Applications, 2011,28(6): 839-844.
[19]	XIAO F Y . Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy[J]. Information Fusion, 2019,46: 23-32.
[20]	XIAO F Y . A new divergence measure for belief functions in D-S evidence theory for multisensor data fusion[J]. Information Sciences, 2020,514: 462-483.
[21]	李军伟, 刘先省, 胡振涛 . 基于Einstein算子的证据冲突度量方法[J]. 系统工程与电子技术, 2017,39(12): 2659-2664.
	LI J W , LIU X X , HU Z T . Measurement of evidence conflict based on Einstein operator[J]. Systems Engineering and Electronics, 2017,39(12): 2659-2664.
[22]	JIANG W . A correlation coefficient for belief functions[J]. International Journal of Approximate Reasoning, 2018,103: 94-106.
[23]	RUBNER Y , TOMASI C , GUIBAS L J . The earth mover’s distance as a metric for image retrieval[J]. International Journal of Computer Vision, 2000,40(2): 99-121.
[24]	YANG J , WANG G Y , ZHANG Q H . Knowledge distance measure in multigranulation spaces of fuzzy equivalence relations[J]. Information Sciences, 2018,448/449: 18-35.
[25]	QU Y P , XU Z , SHANG C J ,et al. Inconsistency guided robust attribute reduction[J]. Information Sciences, 2021,580: 69-91.
[26]	LEVANDOWSKY M , WINTER D . Distance between sets[J]. Nature, 1971,234: 34-35.

A	k	1-Cor	difBetP	difB_f	dBPA	1-r	RB	BEMD
{1}	0.050	0.560	0.605	0.657	0.786	0.669	0.717	0.785
{1,2}	0.050	0.248	0.426	0.598	0.686	0.358	0.633	0.625
{1,2,3}	0.050	0.084	0.248	0.516	0.570	0.142	0.512	0.465
{1,2,3,4}	0.050	0.024	0.125	0.365	0.423	0.071	0.342	0.305
{1,2,…,5}	0.050	0.003	0.125	0.143	0.132	0.069	0.274	0.145
{1,2,…,6}	0.050	0.020	0.258	0.484	0.388	0.109	0.195	0.278
{1,2,…,7}	0.050	0.064	0.355	0.656	0.503	0.299	0.589	0.374
{1,2,…,8}	0.050	0.107	0.425	0.746	0.571	0.323	0.613	0.445
{1,2,…,9}	0.050	0.153	0.480	0.795	0.619	0.356	0.634	0.501
{1,2,…,10}	0.050	0.197	0.525	0.824	0.655	0.393	0.653	0.545
{1,2,…,11}	0.050	0.239	0.560	0.841	0.684	0.430	0.670	0.581
{1,2,…,12}	0.050	0.278	0.591	0.852	0.708	0.465	0.686	0.612
{1,2,…,13}	0.050	0.315	0.617	0.860	0.728	0.497	0.702	0.637
{1,2,…,14}	0.050	0.348	0.639	0.867	0.745	0.526	0.716	0.659
{1,2,…,15}	0.050	0.378	0.658	0.872	0.759	0.552	0.730	0.678
{1,2,…,16}	0.050	0.406	0.675	0.876	0.773	0.576	0.743	0.695
{1,2,…,17}	0.050	0.430	0.689	0.880	0.785	0.597	0.756	0.710
{1,2,…,18}	0.050	0.453	0.702	0.883	0.795	0.616	0.768	0.723
{1,2,…,19}	0.050	0.474	0.714	0.886	0.805	0.634	0.780	0.735
{1,2,…,20}	0.050	0.493	0.725	0.889	0.813	0.637	0.792	0.745

基于推土机距离的证据冲突强度量方法

Strong measurement method of evidence conflict based on earth mover’s distance

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