Belief Interval Based Distance Measures In The Theory Of Belief Functions
Belief Interval Based Distance Measures In The Theory Of Belief Functions Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Belief Interval Based Distance Measures In The Theory Of Belief Functions book. This book definitely worth reading, it is an incredibly well-written.
Belief Interval-Based Distance Measures in the Theory of Belief Functions by Deqiang Han,Jean Dezert,Yi Yang Pdf
In belief functions related fields, the distance measure is an important concept, which represents the degree of dissimilarity between bodies of evidence. Various distance measures of evidence have been proposed and widely used in diverse belief function related applications, especially in performance evaluation. Existing definitions of strict and nonstrict distance measures of evidence have their own pros and cons. In this paper, we propose two new strict distance measures of evidence (Euclidean and Chebyshev forms) between two basic belief assignments based on the Wasserstein distance between belief intervals of focal elements. Illustrative examples, simulations, applications, and related analyses are provided to show the rationality and efficiency of our proposed measures for distance of evidence.
Belief Functions: Theory and Applications by Jiřina Vejnarová,Václav Kratochvíl Pdf
This book constitutes the thoroughly refereed proceedings of the 4th International Conference on Belief Functions, BELIEF 2016, held in Prague, Czech Republic, in September 2016. The 25 revised full papers presented in this book were carefully selected and reviewed from 33 submissions. The papers describe recent developments of theoretical issues and applications in various areas such as combination rules; conflict management; generalized information theory; image processing; material sciences; navigation.
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5 by Florentin Smarandache,Jean Dezert ,Albena Tchamova Pdf
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 (available at fs.unm.edu/DSmT-book4.pdf or www.onera.fr/sites/default/files/297/2015-DSmT-Book4.pdf) in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well.
Belief Functions: Theory and Applications by Fabio Cuzzolin Pdf
This book constitutes the thoroughly refereed proceedings of the Third International Conference on Belief Functions, BELIEF 2014, held in Oxford, UK, in September 2014. The 47 revised full papers presented in this book were carefully selected and reviewed from 56 submissions. The papers are organized in topical sections on belief combination; machine learning; applications; theory; networks; information fusion; data association; and geometry.
Belief Functions: Theory and Applications by Sébastien Destercke,Thierry Denoeux,Fabio Cuzzolin,Arnaud Martin Pdf
This book constitutes the refereed proceedings of the 5th International Conference on Belief Functions, BELIEF 2018, held in Compiègne, France, in September 2018.The 33 revised regular papers presented in this book were carefully selected and reviewed from 73 submissions. The papers were solicited on theoretical aspects (including for example statistical inference, mathematical foundations, continuous belief functions) as well as on applications in various areas including classification, statistics, data fusion, network analysis and intelligent vehicles.
Belief Functions: Theory and Applications by Thierry Denœux,Eric Lefèvre,Zhunga Liu,Frédéric Pichon Pdf
This book constitutes the refereed proceedings of the 6th International Conference on Belief Functions, BELIEF 2021, held in Shanghai, China, in October 2021. The 30 full papers presented in this book were carefully selected and reviewed from 37 submissions. The papers cover a wide range on theoretical aspects on mathematical foundations, statistical inference as well as on applications in various areas including classification, clustering, data fusion, image processing, and much more.
Machine Learning for Cyber Security by Yuan Xu,Hongyang Yan,Huang Teng,Jun Cai,Jin Li Pdf
The three-volume proceedings set LNCS 13655,13656 and 13657 constitutes the refereedproceedings of the 4th International Conference on Machine Learning for Cyber Security, ML4CS 2022, which taking place during December 2–4, 2022, held in Guangzhou, China. The 100 full papers and 46 short papers were included in these proceedings were carefully reviewed and selected from 367 submissions.
Fusion of heterogeneous remote sensing images by credibilist methods by Imen HAMMAMI Pdf
This thesis falls within the framework of a cotutelle agreement between the LabS-TICC laboratory of IMT Atlantique, Brest, France and the LIPAH laboratory of the Faculty of Sciences of Tunis, Tunisia. It would not have been possible without persistent help of a large number of peoples to whom I would like to convey my heartfelt gratitude.
Conflict Decision Method based on Quadratic Combination by Xin Guan,Jing Zhao,Haiqiao Liu Pdf
There are many unsatisfactory situations in the existing improvement methods of evidence theory, such as a large amount of calculation, the normalization process is unreasonable, the evidence combination effect is not ideal in the conflict evidence decision-making process, and so on. This paper proposes a method based on quadratic combination of conflict evidence to improve the above situations. Firstly, a new flow chart of conflict evidence decision method based on quadratic combination is proposed. Secondly, a new multiplicative normalization rule is proposed, and the new rule is analyzed to verify its rationality. Thirdly, the shortcomings of the existing conflict measurement methods are analyzed, a new conflict measurement function is proposed, and the rationality of the new function is analyzed. Finally, through the analysis of the example and comparison with the existing evidence combination rules, the effectiveness of the method of this paper is verified.
Belief Functions: Theory and Applications by Thierry Denoeux,Marie-Hélène Masson Pdf
The theory of belief functions, also known as evidence theory or Dempster-Shafer theory, was first introduced by Arthur P. Dempster in the context of statistical inference, and was later developed by Glenn Shafer as a general framework for modeling epistemic uncertainty. These early contributions have been the starting points of many important developments, including the Transferable Belief Model and the Theory of Hints. The theory of belief functions is now well established as a general framework for reasoning with uncertainty, and has well understood connections to other frameworks such as probability, possibility and imprecise probability theories. This volume contains the proceedings of the 2nd International Conference on Belief Functions that was held in Compiègne, France on 9-11 May 2012. It gathers 51 contributions describing recent developments both on theoretical issues (including approximation methods, combination rules, continuous belief functions, graphical models and independence concepts) and applications in various areas including classification, image processing, statistics and intelligent vehicles.
A novel decision probability transformation method based on belief interval by Zhan Deng,Jianyu Wang Pdf
In Dempster–Shafer evidence theory, the basic probability assignment (BPA) can effectively represent and process uncertain information. How to transform the BPA of uncertain information into a decision probability remains a problem to be solved. In the light of this issue, we develop a novel decision probability transformation method to realize the transition from the belief decision to the probability decision in the framework of Dempster–Shafer evidence theory. The newly proposed method considers the transformation of BPA with multi-subset focal elements from the perspective of the belief interval, and applies the continuous interval argument ordered weighted average operator to quantify the data information contained in the belief interval for each singleton. Afterward, we present an approach to calculate the support degree of the singleton based on quantitative data information. According to the support degree of the singleton, the BPA of multi-subset focal elements is allocated reasonably. Furthermore, we introduce the concepts of probabilistic information content in this paper, which is utilized to evaluate the performance of the decision probability transformation method. Eventually, a few numerical examples and a practical application are given to demonstrate the rationality and accuracy of our proposed method.
Data Mining and Big Data by Ying Tan,Yuhui Shi,Albert Zomaya,Hongyang Yan,Jun Cai Pdf
This two-volume set, CCIS 1453 and CCIS 1454, constitutes refereed proceedings of the 6th International Conference on Data Mining and Big Data, DMBD 2021, held in Guangzhou, China, in October 2021. The 57 full papers and 28 short papers presented in this two-volume set were carefully reviewed and selected from 258 submissions. The papers present the latest research on advantages in theories, technologies, and applications in data mining and big data. The volume covers many aspects of data mining and big data as well as intelligent computing methods applied to all fields of computer science, machine learning, data mining and knowledge discovery, data science, etc.
A fast combination method in DSmT and its application to recommender system by Yilin Dong,Xinde Li, Yihai Liu Pdf
In many applications involving epistemic uncertainties usually modeled by belief functions, it is often necessary to approximate general (non-Bayesian) basic belief assignments (BBAs) to subjective probabilities (called Bayesian BBAs).
Basic belief assignment approximations using 4 degree of non-redundancy for focal element by Yi YANG ,Deqiang HAN,Jean DEZERT Pdf
Dempster-Shafer evidence theory, also called the theory of belief function, is widely used for uncertainty modeling and reasoning. However, when the size and number of focal elements are large, the evidence combination will bring a high computational complexity. To address this issue, various methods have been proposed including the implementation of more efficient combination rules and the simplifications or approximations of Basic Belief Assignments (BBAs). In this paper, a novel principle for approximating a BBA into a simpler one is proposed, which is based on thed egree of non-redundancy for focal elements.
Multi-Attribute Decision Making Method Based on Aggregated Neutrosophic Set by Wen Jiang ,Zihan Zhang, Xinyang Deng Pdf
Multi-attribute decision-making refers to the decision-making problem of selecting the optimal alternative or sorting the scheme when considering multiple attributes, which is widely used in engineering design, economy, management and military, etc. But in real application, the attribute information of many objects is often inaccurate or uncertain, so it is very important for us to find a useful and efficient method to solve the problem. Neutrosophic set is proposed from philosophical point of view to handle inaccurate information efficiently, and a single-valued neutrosophic set (SVNS) is a special case of neutrosophic set, which is widely used in actual application fields. In this paper, a new method based on single-valued neutrosophic sets aggregation to solve multi-attribute decision making problem is proposed. Firstly, the neutrosophic decision matrix is obtained by expert assessment, a score function of single-valued neutrosophic sets (SVNSs) is defined to obtain the positive ideal solution (PIS) and the negative ideal solution (NIS). Then all alternatives are aggregated based on TOPSIS method to make decision. Finally numerical examples are given to verify the feasibility and rationality of the method.