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

Abstract

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

CLC Number:

TP274

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.

Figures/Tables 6

References 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.

Metrics

Recommended 0

No Suggested Reading articles found!

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

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

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 26

Related Articles 8

Metrics

Recommended 0

[1]	Xiaoping YANG,Weijian LIAN,Mengjie LI,Zhihong QIAN. Evidence combination method based on Pignistic function transformation and correlation coefficient [J]. Journal on Communications, 2019, 40(3): 73-79.
[2]	ONGYa-fei S,ANGXiao-dan W,EILei L,UEAi-jun X. Evidence combination based on the degree of credibility and falsity [J]. Journal on Communications, 2015, 36(5): 98-103.
[3]	. Critical nodes selection based on trust evaluation in Ad Hoc networks [J]. Journal on Communications, 2014, 35(Z2): 29-219.
[4]	Zhuo-chao LIU,Li YANG,Qi JIANG,Wei WANG,Chun-jie CAO. Critical nodes selection based on trust evaluation in Ad Hoc networks [J]. Journal on Communications, 2014, 35(Z2): 213-219.
[5]	. Trust evaluation model based on improved D-S evidence theory [J]. Journal on Communications, 2013, 34(7): 19-173.
[6]	Lin ZHANG,Jing-wen LIU,Ru-chuan WANG,Hai-yan WANG. Trust evaluation model based on improved D-S evidence theory [J]. Journal on Communications, 2013, 34(7): 167-173.
[7]	Tian-ning ZANG,Xiao-chun YUN,Yong-zheng ZHANG,Chao-guang MEN,Xiang CUI. Botnets' similarity analysis based on communication features and D-S evidence theory [J]. Journal on Communications, 2011, 32(4): 66-76.
[8]	Jun-feng TIAN,Wei-dong ZHAO,Rui-zhong DU,Hong-yun CAI. New data fusion model of intrusion detection—— IDSFP [J]. Journal on Communications, 2006, 27(6): 115-120.