The International Arab Journal of Information Technology (IAJIT)

..............................
..............................
..............................


A Novel Evidence Distance in Power Set Space Lei Zheng1, Jiawei Zou1, Baoyu Liu1, Yong Hu2, and Yong Deng2 1College of Information Science and Technology, Jinan University, China 2Big Data Decision Institute, Jinan University, China

Distance measure of evidence presented has been used to measure the similarity of two bodies of evidence. However, it is not considered that the probability distribution on a power set is able to assign to its subsets not only single elements. In this paper a novel approach is proposed to measure the distance of evidence. And some properties that the novel approach has, such as nonnegativity, symmetry, triangular inequality, downward compatibility and higher sensitivity, is proved. Numerical example and real application are used to strictly illustrate the efficiency of the new distance.


[1] Akyar H., “FuzzyRisk Analysis for aProduction System Based on the Nagel Pointof a Triangle,” Mathematical Problems in Engineering, vol. 2016, pp. 9, 2016.

[2] Bera A., Bhattacharjee D., and Nasipuri M., “Fusion-Based Hand Geometry Recognition Using Dempster Shafer Theory,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 29, no. 5, pp. 1556005, 2015.

[3] Chou C., “A Generalized Similarity Measure for Fuzzy Numbers,” Journal of Intelligent and Fuzzy Systems, vol. 30, no. 2, pp. 1147-1155, 2016.

[4] Dempster A., “Upper and Lower Probabilities Induced by a Multi-Valued Mapping,” Annals of Mathematical Statistics, vol. 38, no. 2, pp. 325- 339, 1967.

[5] Dong Y., Wang J., Chen F., Hu Y., and Deng Y., “Location of Facility Basedon Simulated Annealing and “ZKW” Algorithms,” Mathematical Problems in Engineering, vol. 2017, pp. 9, 2017.

[6] Fei L., Wang H., Chen L., and Deng Y., “A New Vector Valued Similarity Measure for Intuitionistic Fuzzy Sets Based Onowa Operators,” Iranian Journal of Fuzzy Systems, vol. 16, no. 3, pp. 113-126, 2017.

[7] Goyal K. and Kaushal S., “Aconstrained non- Linear Optimization Model for fuzzy Pairwise Comparison Matrices Using Teaching Learning 14 The International Arab Journal of Information Technology, Vol. 17, No. 1, January 2020 Based Optimization,” Applied Intelligence, vol. 45, no. 3, pp. 652-661, 2016.

[8] Hu Y., Zhang X., Ngai E., Cai R., and Liu M., “Software Project Risk Analysis Using Bayesian Networks with Causality Constraints,” Decision Support Systems, vol. 56, pp. 439-449, 2013.

[9] Jing X., Bi Y., and Deng H., “An Innovative Two-Stage Fuzzy K NN-DST Classifier for Unknown Intrusion Detection,” The International Arab Journal of Information Technology, vol. 13, no. 4, pp. 359-366, 2016.

[10] Jousselme A., Grenier D., and Bossé É., “A new Distance between Two Bodies of Evidence,” Information Fusion, vol. 2, no. 2, pp. 91-101, 2001.

[11] Jousselme A. and Maupin P., “Distances in Evidence Theory: Comprehensive Survey and Generalizations,” International Journal of Approximate Reasoning, vol. 53, no. 2, pp. 118- 145, 2012.

[12] Khatibi V. and Montazer G., “A New Evidential Distance Measure Based on Belief Intervals,” Scientia Iranica, vol. 17, no. 2, pp. 119-132, 2011.

[13] Liu B., Hu Y., and Deng Y., “New Failure Mode and Effects Analysis based on D Numbers Downscaling Method,” International Journal of Computers Communications and Control, vol. 13, no. 2, pp. 205-220, 2018.

[14] Liu T., Deng Y., and Chan F., “Evidential Supplier Selection based on DEMATEL and Game Theory,” International Journal of Fuzzy Systems, vol. 20, no. 4, pp. 1321-1333, 2017.

[15] Liu W., “Analyzing the Degree of Conflict among Belief Functions,” Artificial Intelligence vol. 170, no. 11, pp. 909-924, 2006.

[16] Loudahi M., Klein J., Vannobel J., and Colot C., “Evidential Matrix Metrics as Distances between Meta-Data Dependent Bodies of Evidence,” IEEE Transactions on Cybernetics, vol. 46, no. 1, pp. 109-122, 2016.

[17] Mo H., Lu X., and Deng Y., “A Generalized Evidence Distance,” Journal of Systems Engineering and Electronics, vol. 27, no. 2, pp. 470-476, 2016.

[18] Pichon F., Mercier D., Lefèvre E., and Delmotte F., “Proposition and Learning of some Belief Function Contextual Correction Mechanisms,” International Journal of Approximate Reasoning vol. 72, pp. 4-42, 2015.

[19] Sabahi F. and Akbarzadeh-T M., “A Qualified Description of Extended Fuzzy Logic,” Information Sciences, vol. 244, pp. 60-74, 2013.

[20] Sabahi F. and Akbarzadeh-T M., “Introducing Validity in Fuzzy Probability for Judicial Decision-Making,” International Journal of Approximate Reasoning, vol. 55, no. 6, pp. 1383- 1403, 2014.

[21] Shafer G., A Mathematical Theory of Evidence, Princeton University Press, 1976.

[22] Sunberg Z. and Rogers J., “A Belief Function Distance Metric for Orderable Sets,” Information Fusion, vo. 14, no. 4, pp. 361-373, 2013.

[23] Wang X., Zhu J., Song Y., and Lei L., “Combination of Unreliable Evidence Sources in Intuitionistic Fuzzy MCD-M Framework,” Knowledge-Based Systems, vol. 97, pp. 24-39, 2016.

[24] Yong D., Wenkang S., Zhenfu Z., and Qi L., “Combining Belief Functions Based on Distance of Evidence,” Decision Support Systems, vol. 38, no. 3, pp. 489-493, 2004.

[25] Yuan K., Xiao F., Fei L., Kang B., and Deng Y., “Modeling Sensor Reliability in Fault Diagnosis Based on Evidence Theory,” Sensors, vol. 16, no. 1, pp. 1-13, 2015.

[26] Zadeh L., “A Simple View of the Dempster- Shafer Theory of Evidence and its Implication for the Rule of Combination,” AI Magazine, vol. 7, no. 2, pp. 85-90, 1986.

[27] Zhang Q., Li M., and Deng Y., “Measure the Structure Similarity of Nodes in Complex Networks Based on Relative Entropy,” Physica A: Statistical Mechanics and its Applications, vol. 491, pp. 749-763, 2018.

[28] Zhang R., Ashuri B., and Deng Y., “A novel Method for Forecasting Time Series Based on Fuzzy Logic and Visibility Graph,” Advances in Data Analysis and Classification, vol. 11, no. 4, pp. 759-783, 2018.

[29] Zhang X., “Interactive Patent Classification Based on Multi-Classifier Fusion and Active Learning,” Neurocomputing, vol. 127, pp. 200- 205, 2014.

[30] Zheng X. and Deng Y., “Dependence Assessment in Human Reliability Analysis Based on Evidence Credibility Decay Model and IOWA Operator,” Annals of Nuclear Energy, vol. 112, pp. 673-684, 2018.

[31] Zheng H. and Deng Y., “Evaluation Method Based on Fuzzy Relations between Dempster- Shafer Belief Structure,” International Journal of Intelligent Systems, vol. 33, no. 7, pp. 1343-1363, 2018. A Novel Evidence Distance in Power Set Space 15 Lei Zheng was born in 1996, in Shanxi, P. R. China. She is currently an undergraduate in Jinan University, Guangzhou, China. She majors in Information and Computational Science,and her research interest is in the area of Information Fusion. Jiawei Zou was born in 1997, in Guangdong, P. R. China. He is currently an undergraduate in Jinan University, Guangzhou, China. He majors in Information and Computational Science,and his research interest is in the area of Information Fusion. Baoyu Liu was born in 1996, in Guangdong, P. R. China. He is currently a junior student in Jinan University, Guangzhou, China. He majors in Information and Computational Science,and his research interest is in the area of Information Fusion. Yong Hu is currently a professor and director of the Big Data Decision Institute at Jinan University and director of Guangdong Engineering Technology Research Center for Big Data Precision Healthcare. He proposed the big data "Causal-Indetermistic-Actionable" methodology framework. His research fields include big data-based intelligent medicine, intelligent finance and precision marketing, etc. In recent years, he published more than 50 papers in SCI/SSCI journals, and 4 four of them are ESI highly cited papers. His papers have been cited over 2100 in the past 5 years in Google Scholar. His major research projects are supported by the Major Research Plan of the National Natural Science Foundation of China, the Special Fund for Science and Technology Development in Guangdong Province( Major Projects of Advanced and Key Techniques Innovation, and Guangdong Engineering Technology Research Center for Big Data Precision Healthcare. Yong Deng received the Ph.D. degree in Precise Instrumentation from Shanghai Jiao Tong University, Shanghai, China, in 2003. From 2005 to 2011, he was an Associate Professor in the Department of Instrument Science and Technology, Shanghai Jiao Tong University. From 2010, he was a Professor in the School of Computer and Information Science, Southwest University, Chongqing, China. From 2012, he was an Visiting Professor in Vanderbilt University, Nashville, TN, USA. From 2016, he was also a Professor in School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi’an, China. From 2017, he is the full professor of Institute of Fundamental and Frontier Science, University of Electronic Science and Technology of China, Chengdu, China. Professor Deng has published more than 100 papers in referred journals such as Decision Support Systems, European Journal of Operational Research and Scientific Reports. His research interests include evidence theory, decision making, information fusion and complex system modelling. He severed as the program member of many conference such as International Conference on Belief Functions. He severed as many editorial board members such as Academic Editor of the PLOS ONE. He severed as the reviewer for more than 30 journals such as IEEE Transaction on Fuzzy Systems. Professor Deng has received numerous honors and awards, including the Elsevier Highly Cited Scientist in China in 2014, 2015 and 2016.